A multiplex polymerase chain reaction for the simultaneous detection of the virus and satellite components associated with cotton leaf curl begomovirus disease complex.

Cotton leaf curl disease (CLCuD) is caused by a complex of several whiteflies (Bemisia tabaci Genn.)-transmitted begomovirus species, Cotton leaf curl Multan virus (CLCuMuV), Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Alabad virus (CLCuAlV) by individual of mixed infection, associated with Cotton leaf curl Multan betasatellite (CLCuMB) and several alphasatellites. The disease causes major economic losses in cotton in the Indian subcontinent. For monitoring of epidemiology and development of management strategies of CLCuD, a quick, sensitive and effective method capable of detecting all the begomovirus, betasatellite and alphasatellite components associated with CLCuD is required. With this objective, a multiplex polymerase chain reaction (mPCR) assay was developed for the simultaneous detection of these three viral components associated with CLCuD of cotton. Primers for each component were designed based on the retrieved reference sequences from the GenBank. Each pair of primers, designed for each of the respective component, was evaluated for its sensitivity and specificity in both the component-specific simplex polymerase chain reaction (sPCR) and mPCR assay. This report identified three viral component-specific pairs of primers which, in all combinations, amplified simultaneously the CP gene (780 nts) of the begomovirus, the ßC1gene (375 nts) of the betasatellite and the Rep gene (452 nts) of the alphasatellite associated with CLCuD in the mPCR assays. The amplified products specific to each component produced by these assays were identified based on their amplicon sizes, and the identities of the viral components amplified were confirmed by cloning and sequencing the amplicons obtained in the mPCR. The mPCR assay was validated using naturally CLCuD-affected cotton plants of the fields. This assay will be useful for rapid detection of CLCuD-associated begomovirus, betasatellite and alphasatellite DNA in field samples, extensive resistance screening in resistance breeding programme, and also monitoring epidemiology for detection of virus and its components when symptoms are mild or absent in the plant.

Noncausal predictive image codec.

The paper describes a lossy image codec that uses a noncausal (or bilateral) prediction model coupled with vector quantization. The noncausal prediction model is an alternative to the causal (or unilateral) model that is commonly used in differential pulse code modulation (DPCM) and other codecs with a predictive component. We show how to obtain a recursive implementation of the noncausal image model without compromising its optimality and how to apply this in coding in much the same way as a causal predictor. We report experimental compression results that demonstrate the superiority of using a noncausal model based predictor over using traditional causal predictors. The codec is shown to produce high-quality compressed images at low bit rates such as 0.375 b/pixel. This quality is contrasted with the degraded images that are produced at the same bit rates by codecs using causal predictors or standard discrete cosine transform/Joint Photographic Experts Group-based (DCT/JPEG-based) algorithms.