Exploitation of microbial antagonists for the control of postharvest diseases of fruits: a review

Fungal diseases result in significant losses of fruits and vegetables during handling, transportation and storage. At present, post-production fungal spoilage is predominantly controlled by using synthetic fungicides. Under the global climate change scenario and with the need for sustainable agriculture, biological control methods of fungal diseases, using antagonistic microorganisms, are emerging as ecofriendly alternatives to the use of fungicides. The potential of microbial antagonists, isolated from a diversity of natural habitats, for postharvest disease suppression has been investigated. Postharvest biocontrol systems involve tripartite interaction between microbial antagonists, the pathogen and the host, affected by environmental conditions. Several modes for fungistatic activities of microbial antagonists have been suggested, including competition for nutrients and space, mycoparasitism, secretion of antifungal antibiotics and volatile metabolites and induction of host resistance. Postharvest application of microbial antagonists is more successful for efficient disease control in comparison to pre-harvest application. Attempts have also been made to improve the overall efficacy of antagonists by combining them with different physical and chemical substances and methods. Globally, many microbe-based biocontrol products have been developed and registered for commercial use. The present review provides a brief overview on the use of microbial antagonists as postharvest biocontrol agents and summarises information on their isolation, mechanisms of action, application methods, efficacy enhancement, product formulation and commercialisation.     

A neural network model for the prediction of compression ignition engine performance at different injection timings

Rapid depletion of fossil fuel and continuous increase in gasoline prices have stimulated the search of alternative fuels. This paper deals with the prediction of engine performance, emission and combustion characteristics of compression ignition engine fuelled with fish oil biodiesel using artificial neural network (ANN). Experimental investigations are carried out in a single cylinder constant speed direct injection diesel engine under variable load conditions at different injection timings?21⁰, 24⁰ and 27⁰ bTDC. The performance, combustion and emission characteristics are measured using an exhaust gas analyser, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. For training the neural network, feed-forward back propagation algorithm is used. The developed ANN model predicts the performance, combustions and exhaust emissions with a correlation coefficients (R) of 0.97–0.99 and a mean relative error of 0.62–4.826%. The root mean square errors are found to be low. The developed model has found to predict accurately the engine performance, combustion and emission parameters at different injection timings.     

Process standardization for optimal virus recovery and removal of substrate DNA and bovine serum proteins in Vero cell-derived rabies vaccine

Purification of a rabies vaccine by a single zonal centrifugation run was replaced by two runs with optimal standardization of the sucrose density gradient. As a result, significant reductions in the levels of substrate DNA and bovine serum protein in the Vero cell-derived human rabies vaccine were achieved. Following many trials, for the first run, loading of the 3.2-l capacity K-3 rotor with 1800 ml of 60% sucrose solution and 1400 ml of vaccine PBS buffer solution gave a satisfactory linear gradient. However, after the first run, the substrate DNA and bovine serum contents exceeded the required levels. After protamine sulphate and Tween-80 treatment of the concentrated inactivated material, a second run using the same procedure as in the first run was tried. However, these purification procedures resulted in low virus recovery. To achieve optimal virus recovery, and removal of substrate DNA and bovine serum protein, the peak fractions from the first run as indicated by the haemagglutination, sucrose concentration, and optical density values were pooled and the sucrose concentration of the pooled fractions was increased to 60%. A second (flotation) run was then carried out. Using this method, the virus recovery rate was more than 95% that of the first run, and the levels of cellular DNA and bovine serum protein were well within the acceptable limits of less than 100 pg/dose and one part per million, respectively. The substrate DNA was quantified by both radioactive labeling and non-radioactive biotin labeling methods. For the quantification of calf serum protein, a counter-immunoelectrophoresis method was developed and effectively applied. A potency assay was performed using the National Institutes of Health (NIH) and well-standardized in vitro single radial immuno diffusion (SRD) methods. Finally, an immunogenicity study was conducted with human volunteers and the results were confirmed by a rapid fluorescent focus inhibition test (RFFIT).     

Analytical and numerical procedures in the application of many-body green's function methods to electron-atom scattering problems

The analytical and numerical procedures used in calculation of e^?-He elastic scattering phase shifts in the Generalized Random Phase Approximation (GRPA) and e^?-He inelastic cross sections in the Random Phase Approximation (RPA) are presented. The pertinent equations are first written in general form and analyzed into spin and angular momentum variables. In both elastic and inelastic work the transition density matrix was represented on a basis. Finite basis sets were also used to construct the static-exchange and polarization parts of the optical potential. The elastic scattering phase shifts were then evaluated numerically. In the inelastic work all calculations were done numerically. As an illustration of the usefulness of these methods, some typical results and computational times are given.     

Restoration of images degraded by compound noise sources using Markov random field models

The main theme of this paper is the derivation of a new algorithm for restoring digitized images degraded by both additive and multiplicative noise sources. In order to keep the derivation sufficiently general, the authors also include degradation caused by blur and a class of nonlinearities. The images under consideration are modeled as Markov random fields, while the additive and multiplicative noise sources are assumed to be Gaussian processes with known means and variances. Blurring of images is accomplished by a shift-invariant point-spread function. Test results with degraded images indicate that the algorithm is effective in restoring images degraded by high levels of additive and multiplicative noise.     

Epitope mapping of a 95 kDa antigen in complex with antibody by solution-phase amide backbone hydrogen/deuterium exchange monitored by Fourier transform ion cyclotron resonance mass spectrometry

The epitopes of a homohexameric food allergen protein, cashew Ana o 2, identified by two monoclonal antibodies, 2B5 and 1F5, were mapped by solution-phase amide backbone H/D exchange (HDX) coupled with Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) and the results were compared to previous mapping by immunological and mutational analyses. Antibody 2B5 defines a conformational epitope, and 1F5 defines a linear epitope. Intact murine IgG antibodies were incubated with recombinant Ana o 2 (rAna o 2) to form antigen-monoclonal antibody (Ag-mAb) complexes. mAb-complexed and uncomplexed (free) rAna o 2 were then subjected to HDX. HDX instrumentation and automation were optimized to achieve high sequence coverage by protease XIII digestion. The regions protected from H/D exchange upon antibody binding overlap and thus confirm the previously identified epitope-bearing segments: the first extension of HDX monitored by mass spectrometry to a full-length antigen-antibody complex in solution.     

Document registration using projective geometry

A technique for extracting filled-in information in form documents is presented. The transformation that is required to convert a filled-in form to match the master (blank form) is derived using results from projective geometry. Experimental studies with several forms indicate the proposed technique to be accurate and robust.     

Mutations in the arginine-rich protein gene (ARP) in pancreatic cancer

The ARP gene encodes a highly conserved arginine-rich protein from chromosomal band 3p21.1. At the cytogenetic level this region is frequently deleted in a variety of different solid tumors, although not in pancreatic cancer. We have reported the presence of a specific mutation (ATG50-->AGG) or deletion of codon 50 of the ARP gene in different tumor types (Shridhar et al., 1996, 1996a). In the present study, we have observed mutations involving codon 50 in 11 of 37 pancreatic tumors. The frequency of codon 50 mutation is roughly the same in pancreatic tumors as in the other types of tumors previously examined. In addition, we have detected mutations at codon 51 in multiple PCR subclones in two other pancreatic tumors. Mutations in the ARP gene are thus commonly observed in pancreatic cancer, as well as many other cancers.