Thirty-one rhizobial isolates nodulating native Lupinus angustifolius (blue lupine) plants growing in Northern Tunisian soils were isolated and analysed using different chromosomal and symbiotic gene markers. Phylogenetic analyses based on recA partial sequences grouped them into at least five groups: four of them within the genus Bradyrhizobium (26 isolates) and one into the genus Microvirga (5 isolates). Representative strains were analysed by multilocus sequence analysis of three housekeeping genes rrs-recA-glnII and rrs-gyrB-dnaK for Bradyrhizobium and Microvirga isolates, respectively. Based on this analysis, eight isolates clustered with the previously described strains Bradyrhizobium lupini USDA3051 and Bradyrhizobium canariense BTA-1. However, five of the isolates clustered separately and may constitute a new species within the Bradyrhizobium genus. The remaining five isolates were closely related to the strain Microvirga sp. LmiM8 and may constitute a new Microvirga species. The analysis of the nodC gene showed that all Bradyrhizobium strains nodulating blue lupine belong to the symbiovar genistearum, whereas the Microvirga isolates are associated with the symbiovar mediterranense. The results of this study support that the L. angustifolius root nodule symbionts isolated in Northern Tunisia belong mostly to the B. canariense/B. lupini lineages. However, new clades of Bradyrhizobium and Microvirga have been identified as L. angustifolius endosymbionts.
The software industry plays a vital role in driving technological advancements. Software projects are complex and consist of many components, so change is unavoidable in these projects. The change in software requirements must be predicted early to preserve resources, since it can lead to project failures. This work focuses on small-scale software systems in which requirements are changed gradually. The work provides a probabilistic prediction model, which predicts the probability of changes in software requirement specifications. The first part of the work considers analyzing the changes in software requirements due to certain variables with the help of stakeholders, developers, and experts by the questionnaire method. Then, the proposed model incorporates their knowledge in the Bayesian network as conditional probabilities of independent and dependent variables. The proposed approach utilizes the variable elimination method to obtain the posterior probability of the revisions in the software requirement document. The model was evaluated by sensitivity analysis and comparison methods. For a given dataset, the proposed model computed the low state revisions probability to 0.42, and the high state revisions probability to 0.45. Thus, the results proved that the proposed approach can predict the change in the requirements document accurately by outperforming existing models.
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