scholarly journals Optimal data partitioning, multispecies coalescent and Bayesian concordance analyses resolve early divergences of the grape family (Vitaceae)

Cladistics ◽  
2017 ◽  
Vol 34 (1) ◽  
pp. 57-77 ◽  
Author(s):  
Limin Lu ◽  
Cymon J. Cox ◽  
Sarah Mathews ◽  
Wei Wang ◽  
Jun Wen ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Verônica A. Thode ◽  
Caetano T. Oliveira ◽  
Benoît Loeuille ◽  
Carolina M. Siniscalchi ◽  
José R. Pirani

AbstractWe assembled new plastomes of 19 species of Mikania and of Ageratina fastigiata, Litothamnus nitidus, and Stevia collina, all belonging to tribe Eupatorieae (Asteraceae). We analyzed the structure and content of the assembled plastomes and used the newly generated sequences to infer phylogenetic relationships and study the effects of different data partitions and inference methods on the topologies. Most phylogenetic studies with plastomes ignore that processes like recombination and biparental inheritance can occur in this organelle, using the whole genome as a single locus. Our study sought to compare this approach with multispecies coalescent methods that assume that different parts of the genome evolve at different rates. We found that the overall gene content, structure, and orientation are very conserved in all plastomes of the studied species. As observed in other Asteraceae, the 22 plastomes assembled here contain two nested inversions in the LSC region. The plastomes show similar length and the same gene content. The two most variable regions within Mikania are rpl32-ndhF and rpl16-rps3, while the three genes with the highest percentage of variable sites are ycf1, rpoA, and psbT. We generated six phylogenetic trees using concatenated maximum likelihood and multispecies coalescent methods and three data partitions: coding and non-coding sequences and both combined. All trees strongly support that the sampled Mikania species form a monophyletic group, which is further subdivided into three clades. The internal relationships within each clade are sensitive to the data partitioning and inference methods employed. The trees resulting from concatenated analysis are more similar among each other than to the correspondent tree generated with the same data partition but a different method. The multispecies coalescent analysis indicate a high level of incongruence between species and gene trees. The lack of resolution and congruence among trees can be explained by the sparse sampling (~ 0.45% of the currently accepted species) and by the low number of informative characters present in the sequences. Our study sheds light into the impact of data partitioning and methods over phylogenetic resolution and brings relevant information for the study of Mikania diversity and evolution, as well as for the Asteraceae family as a whole.


2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.


2016 ◽  
Vol 94 ◽  
pp. 447-462 ◽  
Author(s):  
Scott V. Edwards ◽  
Zhenxiang Xi ◽  
Axel Janke ◽  
Brant C. Faircloth ◽  
John E. McCormack ◽  
...  

2019 ◽  
Vol 478 ◽  
pp. 208-221 ◽  
Author(s):  
Han Liu ◽  
Shyi-Ming Chen ◽  
Mihaela Cocea

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