Identification of Pakistani Isolates of Xanthomonas oryzae pv. oryzae by Insertion Sequence based polymerase chain reaction (IS-PCR)

Xanthomonas oryzae pv. oryzae is the causal agent of bacterial blight disease in rice. The pathogen was isolated from infected samples collected after extensive survey of rice growing areas of Pakistan. Afterwards, Insertion sequence based Polymerase chain reaction was applied using the high copy insertion sequence IS1113 based primer for molecular identification of the isolates as well as to differentiate them from Xanthomonas oryzae pv. Oryzicol

Detection of phytoplasma in citrus orchards of Pakistan

Citrus fruits are one of the major export commodities of Pakistan. However, being such an important crop citrus is affected by a number of destructive diseases and phytoplasmal disease is one of them. In Pakistan no significant research has been conducted on phytoplasmal diseases of citrus. Therefore, present study was conducted to confirm the presence of phytoplasmal particles in diseased samples of citrus from Sahiwal, Pakpattan, Multan and Khaenwal districts the most important citrus growing areas of Pakistan. For this purpose DNA was extracted from leaf samples collected from the three districts and single (O-MLO) and nested PCR were applied to detect phytoplasmal particles. With O-MLO primers a 558bp fragment was amplified from 16S rRNA phytoplasmal gene and 1.2kb phytoplasmal DNA fragment was amplified with nested PCR. The results revealed the presence of citrus phytoplasma in Southern Punjab region of Pakistan. In order to confirm the alternate hosts of citrus phytoplasma as well as the insect vectors involved in the transmission of the disease, weeds as well as insects were collected from citrus orchards for molecular detection of phytoplasma and their analysis are is in progress

Comparative Susceptibility of Different Cell Cultures and Chicken Embryo Organ Cultures to Infectious Bursal Disease Virus of Poultry

Infectious bursal disease (IBD) is an acute highly contagious viral infection of young chickens often resulting in immunosuppression. Inactivated vaccines play significant role in protection against IBD. Mammalian cell lines could be used for producing such vaccines. In present study twenty-five, local strains of IBD virus were inoculated into chicken embryo bursa cell culture, liver cell culture, kidney cell culture, fibroblast cell culture and Vero cell lines for cytopathic effect. Moreover comparative susceptibility of chicken embryo bursa organ, embryo liver organ and embryo kidney organ cultures, to infectious bursal disease virus were studied. Chicken embryo bursa cell line was found to be most susceptible (90%) followed by Vero cell lines (70%), fibroblast cell lines (65%), kidney cell lines (50%) and liver cell lines (45%). While chicken embryo bursa organ culture gave maximum cytopathic effect (80%) followed by chicken embryo liver (60%) and kidney organ (45%). From these studies it is concluded that after bursa cell lines, Vero cell lines gave maximum cytopathic effect yielding high number of virus particles and are easy to maintain. Thus Vero cell lines can be used to produce infectious bursal disease vaccines using local isolates

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

The protection problems in microgrid effect the reliability of the power system caused due to high distributed generator penetrations. Therefore, fault protection in microgrid is extremely important and needs to be resolved to enhance the robustness of the power system. This manuscript proposes a combined signal processing and data mining-based approach for microgrid fault protection. In this study, first the multiresolution decomposition of wavelet transform is employed to preprocess the voltage and current signals to compute the total harmonic distortion of the voltage and current. Then, the statistical indices of standard deviation, mean, and median of the total harmonic distortion and the negative sequence components of active and reactive power are used to collect the input data. After that, all the available data is provided to the random forest-based classifier to evaluate the efficiency of the proposed scheme in terms of the detection, identification, and classification of faults. This study used different aspects for data collection by simulating various fault and no-fault cases for both looped and radial configurations under grid-connected and islanded modes of operation. The simulations were performed on a standard medium voltage microgrid using MATLAB/SIMULINK, whereas the analysis for testing and training of the random forest were conducted in Python. It is recognized that the proposed method performed better than support vector machines and decision tree that are reported in the literature. The results further demonstrate that the proposed method can also detect simultaneous faults, and it is also effective against measurement noise