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Phylogenetic studies of Phoma species by maximum likelihood analysis
37-46Views:133The cosmopolitan Phoma genus contains mainly phytopathogenic, opportunistic parasite, and saprophyte fungal species. Up to now the characterization of Phoma species and other taxa of Phoma has so far been determined on the basis of morphology on standardized media, and gene sequence analysis was only used as a confirmative or distinctive complement.
In this study we have tried to study phylogenetic relationships by maximum likelihood method in the Phoma genus. We employed a part of the gene responsible for the synthesis of translation elongation factor 1 subunit alpha protein (tef1) containing both introns and exons, a part of the gene responsible for synthesis of tubulin protein and ITS region containing the internal transcribed spacer regions 1 and 2 and the 5.8S rDNA as potential genetic markers to infer phylogenetic relationships among different Phoma taxa. Twenty-four isolates of eleven different Phoma species were firstly characterised by morphologically, and then their tef1, tubulin and ITS sequences were sequenced and analysed by maximum likelihood method carried out by PAUP*4.0b program. According to constructed phylogenetic trees, the different Phoma taxons are well separated. However these trees do not support the traditional Phoma sections based on morphological characterization.
The maximum likelihood analyses of all three sequences confirmed that the Phyllosticta sojicola species is clustered with the Phoma exigua var. exigua group and the Phoma sojicola is grouped with Phoma pinodella group. The experienced molecular evidences initiate the demand of reclassification of formerly mentioned soybean pathogens. -
Estimation of direct and maternal genetic parameters for weaning weight in Hungarian Simmental cattle
17-22Views:175The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maximum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.
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Estimating Genetic Parameters using a Random Regression Model
53-55Views:135One of the most important part of the genetic evaluation using a random regression model is the estimation of variance components. This is the topic of many papers because the large computational costs. We can use restricted maximum likelihood (REML), Gibbs sampling and ℜ method for the estimation of genetic parameters. The variance components are necessary to calculate the heritabilities and repeatabilities.
The aim of our paper is to estimate the variance components using a random regression repeatability model from test day data set of Hungarian Holstein-Friesian dairy cows and to analyse the change of additive genetic and permanent environmental variance, heritability and repeatability over lactation. -
Study of alternative oxidase as possible molecular marker for phylogenetic analysis of the Botrytis cinerea
127-132Views:143Botrytis cinerea (teleomorph Botryotinia fuckeliana (de Bary) Whetzel) is able to attack several economically important plants causing gray rot. Botrytis cinerea species complex includes two cryptic species (B. cinerea and B. pseudocinerea) that tolerate fungicides differently. On the basis of classical taxonomic markers, the two related species are very difficult to be distinguished; therefore, their separation is usually performed using molecular methods based on the time-consuming molecular analysis of several markers. Our goal was to find markers, which are suitable for the differentiation. Testing the nucleotide sequences of the alternative oxidase encoding gene, B. cinerea and B. pseudocinerea strains were clearly differentiated. Moreover, the analysis of the protein sequences of the enzyme with the maximum likelihood method reflected well the taxonomic relationships of the different fungi.
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An advanced classification method for urban land cover classification
51-57Views:214This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment.
The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features.
Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.