Abstract:
Background Pigeonpea is an important grain legume of the semi-arid tropics and sub-tropical regions where it plays a crucial role in the food and nutritional security of the people. The average productivity of pigeonpea has remained very low and stagnant for over five decades due to lack of genomic information and intensive breeding efforts. Previous SSR-based linkage maps of pigeonpea used inter-specific crosses due to low inter-varietal polymorphism. Here our aim was to construct a high density intra-specific linkage map using genic-SNP markers for mapping of major quantitative trait loci (QTLs) for key agronomic traits, including plant height, number of primary and secondary branches, number of pods, days to flowering and days to maturity in pigeonpea. Results A population of 186 F2:3 lines derived from an intra-specific cross between inbred lines ‘Pusa Dwarf’ and ‘HDM04-1’ was used to construct a dense molecular linkage map of 296 genic SNP and SSR markers covering a total adjusted map length of 1520.22 cM for the 11 chromosomes of the pigeonpea genome. This is the first dense intra-specific linkage map of pigeonpea with the highest genome length coverage. Phenotypic data from the F2:3 families were used to identify thirteen QTLs for the six agronomic traits. The proportion of phenotypic variance explained by the individual QTLs ranged from 3.18% to 51.4%. Ten of these QTLs were clustered in just two genomic regions, indicating pleiotropic effects or close genetic linkage. In addition to the main effects, significant epistatic interaction effects were detected between the QTLs for number of pods per plant. Conclusions A large amount of information on transcript sequences, SSR markers and draft genome sequence is now available for pigeonpea. However, there is need to develop high density linkage maps and identify genes/QTLs for important agronomic traits for practical breeding applications. This is the first report on identification of QTLs for plant type and maturity traits in pigeonpea. The QTLs identified in this study provide a strong foundation for further validation and fine mapping for utilization in the pigeonpea improvement.

Pigeonpea
[Cajanus cajan (L.) Millspaugh] is an
important food legume of the semi-arid tropics (SAT) sustaining livelihood of millions of people. Stagnant and unstable
yield per hectare all over the world is the characteristic feature of this crop.
This is primarily ascribed to its susceptibility/sensitivity to a number of biotic
and abiotic factors. Among biotic factors, insects such as pod borer (Helicoverpa armigera), pod fly (Melanoagromyza obtusa) and spotted borer
(Maruca vitrata) substantially damage
the crop and result in significant economic losses. Management of these insects
by genetic means has always been considered environment friendly approach. However,
genetic improvement has always been impeded by limited genetic variability in the primary
gene pool of pigeonpea. Wild species present in the secondary and tertiary gene
pools have been reported to carry resistance for such insects. However, transfer
of resistance through conventional backcrossing has not been
much successful. It calls for gene introgression through marker assisted backcrossing
(MABC) or advanced backcross breeding (AB breeding). In this review, we have attempted
to assess the progress made through conventional and molecular breeding and suggested
the ways to move further towards genetic enhancement for insects resistance in pigeonpea

Abstract:
In this study, 43,324 transcriptome shotgun assembly unigene contigs were assembled from 1.696 million 454 GS-FLX sequence reads of separate pooled cDNA libraries prepared from leaf, root, stem and immature seed of two pigeonpea varieties, Asha and UPAS 120. A total of 3,771 genic-SSR loci, excluding homopolymeric and compound repeats, were identified; of which 2,877 PCR primer pairs were designed for marker development. Dinucleotide was the most common repeat motif with a frequency of 60.41%, followed by tri- (34.52%), hexa- (2.62%), tetra- (1.67%) and pentanucleotide (0.76%) repeat motifs. Primers were synthesized and tested for 772 of these loci with repeat lengths of ≥18 bp. Of these, 550 markers were validated for consistent amplification in eight diverse pigeonpea varieties; 71 were found to be polymorphic on agarose gel electrophoresis. Genetic diversity analysis was done on 22 pigeonpea varieties and eight wild species using 20 highly polymorphic genic-SSR markers. The number of alleles at these loci ranged from 4-10 and the polymorphism information content values ranged from 0.46 to 0.72. Neighbor-joining dendrogram showed distinct separation of the different groups of pigeonpea cultivars and wild species. Deep transcriptome sequencing of the two parental lines helped in silico identification of polymorphic genic-SSR loci to facilitate the rapid development of an intra-species reference genetic map, a subset of which was validated for expected allelic segregation in the reference mapping population.We developed 550 validated genic-SSR markers in pigeonpea using deep transcriptome sequencing. From these, 20 highly polymorphic markers were used to evaluate the genetic relationship among species of the genus Cajanus. A comprehensive set of genic-SSR markers was developed as an important genomic resource for diversity analysis and genetic mapping in pigeonpea.Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important food legume predominantly cultivated in the t

Abstract:
From the point of view of canonical quantum gravity, it has become imperative to find a framework for quantization which provides a {\em general} prescription to find the physical inner product, and is flexible enough to accommodate non-canonical variables. In this dissertation I consider an algebraic formulation of the Dirac approach to the quantization of constrained systems, due to A. Ashtekar. The Dirac quantization program is augmented by a general principle to find the inner product on physical states. Essentially, the Hermiticity conditions on physical operators determine this inner product. I also clarify the role in quantum theory of possible algebraic identities between the elementary variables. I use this approach to quantize various finite dimensional systems. Some of these models test the new aspects of the algebraic framework. Others bear qualitative similarities to \gr, and may give some insight into the pitfalls lurking in \qg. In (spatially compact) general relativity, the Hamiltonian is constrained to vanish. I present various approaches one can take to obtain an interpretation of the quantum theory of such ``dynamically constrained'' systems. I apply some of these ideas to the Bianchi I cosmology, and analyze the issue of the initial singularity in quantum theory.

Abstract:
We solve the complex Einstein equations for Bianchi I and II models formulated in the Ashtekar variables. We then solve the reality conditions to obtain a parametrization of the space of Lorentzian solutions in terms of real canonically conjugate variables. In the Ashtekar variables, the dynamics of the universe point particle is governed by only a curved supermetric -- there is no potential term. In the usual metric formulation the particle bounces off a potential wall in flat superspace. We consider possible characterizations of this ``bounce'' in the potential-free Ashtekar variables.

Abstract:
An extension of the Dirac procedure for the quantization of constrained systems is necessary to address certain issues that are left open in Dirac's original proposal. These issues play an important role especially in the context of non-linear, diffeomorphism invariant theories such as general relativity. Recently, an extension of the required type was proposed by one of us using algebraic quantization methods. In this paper, the key conceptual and technical aspects of the algebraic program are illustrated through a number of finite dimensional examples. The choice of examples and some of the analysis is motivated by certain peculiar problems endemic to quantum gravity. However, prior knowledge of general relativity is not assumed in the main discussion. Indeed, the methods introduced and conclusions arrived at are applicable to any system with first class constraints. In particular, they resolve certain technical issues which are present also in the reduced phase space approach to quantization of these systems.

Abstract:
The long-range forecasts (LRF) based on statistical methods for southwest monsoon rainfall over India (ISMR) has been issued by the India Meteorological Department (IMD) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the operational models of IMD failed to predict the deficient monsoon years 2002 and 2004 on the earlier occasions and so had happened for monsoon 2009. In this paper a brief of the recent methods being followed for LRF that is 8-parameter and 10-parameter power regression models used from 2003 to 2006 and new statistical ensemble forecasting system are explained. Then the new three stage procedure is explained. In this the most pertinent predictors are selected from the set of all the potential predictors for April, June and July models. The model equations are developed by using the linear regression and neural network techniques based upon training set of the 43 years of data from 1958 to 2000. The skill of the models is evaluated based upon the validation set of 11 years of data from 2001 to 2011, which has shown the high skill on the validation data set. It can be inferred that these models have the potential to provide a prediction of ISMR, which would significantly improve the operational forecast.

Abstract:
This paper presents an outline for removing noise in digital images. We see that the real time images are get more corrupted while acquisition, processing and transmission. As we deal 98% of our daily work on real time application data. So to suppress noise and elimination of noise from real time images is a research area for the developers, that how to predict noise and to remove that one noise from the image. As the real time images deals with spatial domain area. So in this paper we present a non-liner filter that may used to detect and remove the noise from digital images. The proposed filter is implemented in MatLab. There are so many filters available in literature, some are used to remove substitutive noise and some for additive noise but no filter is used to reduce noise from real time images. So we give a spatial filter for digital image de-noising used for real time applications

Abstract:
We study the problem of computing the probability for the time-of-arrival of a quantum particle at a given spatial position. We consider a solution to this problem based on the spectral decomposition of the particle's (Heisenberg) state into the eigenstates of a suitable operator, which we denote as the ``time-of-arrival'' operator. We discuss the general properties of this operator. We construct the operator explicitly in the simple case of a free nonrelativistic particle, and compare the probabilities it yields with the ones estimated indirectly in terms of the flux of the Schr\"odinger current. We derive a well defined uncertainty relation between time-of-arrival and energy; this result shows that the well known arguments against the existence of such a relation can be circumvented. Finally, we define a ``time-representation'' of the quantum mechanics of a free particle, in which the time-of-arrival is diagonal. Our results suggest that, contrary to what is commonly assumed, quantum mechanics exhibits a hidden equivalence between independent (time) and dependent (position) variables, analogous to the one revealed by the parametrized formalism in classical mechanics.