Classification of ECG signals using Hermite functions and MLP neural networks

Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of the ECG signals. The feature extraction module extracts a balanced combination of the Hermit features and three timing interval feature. Then a number of multi-layer perceptron (MLP) neural networks with different number of layers and eight training algorithms are designed. Seven files from the MIT/BIH arrhythmia database are selected as test data and the performances of the networks, for speed of convergence and accuracy classifications, are evaluated.  Generally all of the proposed algorisms have good training time, however, the resilient back propagation (RP) algorithm illustrated the best overall training time among the different training algorithms. The Conjugate gradient back propagation (CGP) algorithm shows the best recognition accuracy about 98.02% using a little amount of features

Improvement in salt and drought tolerance of alfalfa (Medicago sativa L.) using tissue culture and molecular genetic techniques

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN005652 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Molecular Identification of Turnip Mosaic virus (TuMV) in Hoary Mustard (Herschfeldia incana) From Iran

Turnip mosaic virus (TuMV) is amember of the Potyvirus genus within the Potyviridae family and is one the most important viruses infecting Brassicaceae plants. In April 2012, suspicious symptoms of a viral disease such as mosaic, stunting and malformation were observed on Herschfeldia incana. The collected samples were tested using reverse transcription-polymerase chain reaction (RT-PCR) with specific primers corresponding to TuMV coat protein gene. Amplified fragment (986bp) was first purified and then directly sequenced. Analysis of its CP nucleotide and amino acid sequence revealed 85.42-89.58 % and 91.64-95.12% similarity to those of 31 TuMV isolates from other countries respectively. Phylogenetic tree was constructed by MEGA6 software using neighbor joining method. The results showed that the TuMV isolate and 3 Iranian isolates have been clustered into the basal-B group

Selection of Specific Single Chain Variable Fragments (SCFV) Against Polymyxa Betae from Phage Display Libraries

Sugar beet is one of the most important industrial crops in Iran. For the last two decades it has been mainly affected by a destructive virus, beet necrotic yellow vein virus (BNYVV). The Polymyxa betae is the only natural transmitting agent of the disease among the plants. Developing accurate diagnostic methods may have a major impact on the rising of resistant germplasms. In the present study, specific monoclonal recombinant antibodies in the form of single chain variable fragments (scFv) were obtained from naïve phage display libraries. The fungus specific glutathione-S-transferase (GST) protein was chosen as an antigen for developing antibodies and diagnostic purposes. To generate specific scFv, screening of Tomlinson phage display libraries was performed by applying both recombinant and native fungal GST. Using the recombinant GST in the panning process resulted in the isolation of an antibody only bound to recombinant GST but it failed to detect native GST in the infected plants. Alternatively, the process of panning was carried out by applying native fungal GST trapped to immunotubes through specific polyclonal antibody intermediate. The recent approach resulted in the selection of a specific scFv binding to native GST which was able to detect the presence of the fungi within infected plants. To the best of our knowledge, this is the first report on the generation of recombinant antibodies against Polymyxa betae, fungal vector of sugar beet rhizomania disease