Genetic diversity and evolutionary analyses of potyviruses infecting narcissus in Iran.

Potyviruses are among the most important pathogens of dicotyledonous and monocotyledonous ornamentals and crop plants. In this study, leaf samples were collected from symptomatic narcissus plants and weeds in Fars and Tehran provinces of Iran. Enzyme-linked immunosorbent assay using broad-spectrum potyvirus antibodies gave a positive reaction with 38 out of 61 narcissus samples tested (62.3%); the results were confirmed by reverse-transcription polymerase chain reaction using universal NIb primers, and for thirty samples, by sequencing and phylogenetic studies. The results suggested the infection of almost all positive samples with narcissus yellow stripe virus (NYSV); only one sample seemed to be infected with narcissus late season yellows virus (NLSYV). The 3'-end of the genome of the NLSYV isolate and six NYSV isolates, encompassing the complete coat protein gene, was amplified and sequenced using species-specific and universal potyvirus primers. Sequence analysis indicated the presence of NLSYV and NYSV, not previously identified from Western Asia. No evidence of recombination was found in Iranian isolates. Based on phylogenetic analyses, isolates of NLSYV and NYSV clustered into five and three phylogroups, respectively, where all the Iranian isolates fell into distinct subpopulations in groups NLSYV-I and NYSV-II. Multiple sequence alignments showed some phylogroup-specific amino acid substitutions for both viruses. Phylogroup IV and II populations had higher nucleotide diversities as compared with other populations of NLSYV and NYSV, respectively. Our findings revealed the presence of negative selection in the populations of both viruses. Almost no statistically significant gene flow was found between populations of these viruses. Supplementary information: The online version contains supplementary material available at 10.1007/s42161-021-00985-0.

Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services.

In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

Optimum Concentrations of Trichoderma longibrachiatum and Cadusafos for Controlling Meloidogyne javanica on Zucchini Plants.

A factorial experiment was established in a completely randomized design to verify the effect of different inoculum levels of an Iranian isolate of Trichoderma longibrachiatum separately and in combination with various concentrations of cadusafos against Meloidogyne javanica in the greenhouse. Zucchini seeds were soaked for 12 hr in five densities (0, 10(5), 10(6), 10(7), and 10(8) spores/ml suspension) of the fungus prior to planting in pots containing four concentrations of cadusafos (0, 0.5, 1, and 2 mg a.i./kg soil). The data were analyzed using a custom response surface regression model and the response surface curve and contour plots were drawn. Reliability of the model was examined by comparing the result of new experimental treatments with the predicted results. The optimal levels of these two variables also were calculated. The interactive effects of concentrations of Trichoderma and cadusafos were insignificant for several responses such as the total number of eggs per gram soil, the number of intact eggs per gram soil, nematode reproduction factor, and control percent. Closeness of experimental mean values with the expected values proved the validity of the model. The optimal levels of the cadusafos concentration and Trichoderma concentration that caused the best plant growth and lowest nematode reproduction were 1.7 mg a.i./kg soil and 10(8) conidia/ml suspension, respectively.

Pathogenicity of Pochonia species on eggs of Meloidogyne javanica.

Among fungi, species of the genus Pochonia Batista & O.M. Fonseca are considered as promising biological control agents with high potential to reduce root-knot nematode (RKN) and nematode populations. In this research we investigated Fars province of Iran for the presence of Pochonia spp., compared pathogenicity of different Pochonia species on eggs of RKN in vitro, and selected the best isolates for further studies. During 2004-2006, 128 soil samples of fields infested with cyst nematodes and 18 soil samples infested with RKN were collected from Fars province of Iran. In vitro pathogenicity tests were carried out on 36 isolates of Pochonia spp. obtained from CBS and IRAN culture collections. The seven best isolates of this experiment were selected for greenhouse test and their ability in controlling RKN was examined in natural soil. In greenhouse test fresh weight of plant's tops and roots, gall index, nematode multiplication, second-stage juveniles' population in soil, reproduction rate (P(f)/P(i)), proportion of infected eggs, control efficacy, root colonization and soil colony forming units were determined. In vitro pathogenicity of Pochonia on RKN eggs varied between 39% and 95% eggs infected. In greenhouse experiment, three isolates are promising for control of RKN and selected isolates are subjected to more extensive testing to determine their effectiveness in a range of conditions before being developed as commercial biological control agents.