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Molecular identification of Candidatus Phytoplasma spp. associated with Sophora yellow stunt in Iran

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Allahverdi T., Rahimian H., Rastgou M.
Journal of Plant Protection Research
|
2017
|
tom 57
|
nr 2
In the spring of 2012, sophora (Sophora alopecuroides L.) plants showing symptoms of leaf yellowing, little leaves and stunting were observed in Firooz-kuh (Tehran province), Sari (Mazandaran province) and Urmia (West Azerbaijan province) in Iran. Symptomatic plants from the three locations were subjected to nested polymerase chain reaction (PCR) to amplify 16SrRNA using primer pair P1/P7 followed by primer pair R16F2n/R16R2. Th e amplicons were purifi ed, sequenced and the nucleotide sequences were analyzed by virtual restriction fragment length polymorphism (RFLP). Th e phytoplasmas associated with the yellows disease were identifi ed as members of the 16SrIX group (Candidatus Phytoplasma phoenicium) and the 16SrXII group (Candidatus Phytoplasma solani). Th e two phytoplasmas were placed in 16SrIX-C and 16SrXII-A subgroups, respectively, in constructed phylogenetic trees. Th is is the fi rst report on sophora yellows associated with Candidatus Phytoplasma phoenicium.
2

Fitting soil particle-size distribution (PSD) models by PSD curve fitting software

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Rastgou M., Bayat H., MansoorizadehM. M.
Polish Journal of Soil Science
|
2019
|
tom 52
|
nr 2
This paper describes a particle-size distribution (PSD) curve fitting software for analyzing the soil PSD and soil physical properties. A better characterization of soil texture can be obtained by describing the soil PSD using mathematical models. The mathematical equations of soil PSD are mainly used as a basis to estimate the soil hydraulic properties. Until now, many attempts are made to represent PSD curves using mathematical models, but selecting the best PSD model requires fitting all models to the PSD data, which would be difficult and time-consuming. So far, no specific program has been developed to fit the PSD models to the experimental data. A practical user-friendly software called "PSD Curve Fitting Software" was developed and introduced to program a simultaneous fitting of all models on soil PSD data of all samples. Some of the capabilities of this software are calculating evaluation statistics for all models and soils and their statistical properties such as average, standard deviation, minimum and maximum for all models, the amount of models’ fitting parameters and their statistical properties for all soil samples, soil water retention curve by Arya and Paris (1981) and Meskini-Vishkaee et al. (2014) methods, soil hydraulic conductivity by Arya et al. (1999) method, different textural and hydraulic properties, specific surface area, and other descriptive statistics of PSD for all soil samples. All calculated parameters are presented in an output Excel file format by the software. The software runs under Windows XP/7/8/10.
3

Parametric estimation of water retention using mGMDH method and principal component analysis

76%
Neyshaburi M. R., Bayat H., Rastgou M., Mohammadi K., Gregory A. S., Nariman-Zadeh N.
Polish Journal of Soil Science
|
2016
|
tom 49
|
nr 1
Performing a primary analysis, such as principal component analysis (PCA) may increase accuracy and reliability of developed pedotransfer functions (PTFs). This study focuses on the usefulness of the soil penetration resistance (PR) and principal components (PCs) as new inputs along with the others to develop the PTFs for estimating the soil water retention curve (SWRC) using a multi-objective group method of data handling (mGMDH). The Brooks and Corey (1964) SWRC model was used to give a description of the water retention curves and its parameters were determined from experimental SWRC data. To select eight PCs, PCA was applied to all measured or calculated variables. Penetration resistance, organic matter (OM), aggregates mean weight diameter (MWD), saturated hydraulic conductivity (Ks), macro porosity (Mp), micro porosity (Mip) and eight selected PCs were used as predictors to estimate the Brooks and Corey model parameters by mGMDH. Using PR or OM, Ks and MWD, improved the estimation of SWRC in some cases. Using the predicted PR can be useful in the estimation of SWRC. Using either the MP and Mip or the eight PCs significantly improved the PTFs accuracy and reliability. It would be very useful to apply PCA on the original variables as a primary analysis to develop parametric PTFs.
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