Genetic diversity and population structure of Musa accessions in ex situ conservation

Abstract Background Banana cultivars are mostly derived from hybridization between wild diploid subspecies of Musa acuminata (A genome) and M. balbisiana (B genome), and they exhibit various levels of ploidy and genomic constitution. The Embrapa ex situ Musa collection contains over 220 accessions, of which only a few have been genetically characterized. Knowledge regarding the genetic relationships and diversity between modern cultivars and wild relatives would assist in conservation and breeding strategies. Our objectives were to determine the genomic constitution based on Internal Transcribed Spacer (ITS) regions polymorphism and the ploidy of all accessions by flow cytometry and to investigate the population structure of the collection using Simple Sequence Repeat (SSR) loci as co-dominant markers based on Structure software, not previously performed in Musa. Results From the 221 accessions analyzed by flow cytometry, the correct ploidy was confirmed or established for 212 (95.9%), whereas digestion of the ITS region confirmed the genomic constitution of 209 (94.6%). Neighbor-joining clustering analysis derived from SSR binary data allowed the detection of two major groups, essentially distinguished by the presence or absence of the B genome, while subgroups were formed according to the genomic composition and commercial classification. The co-dominant nature of SSR was explored to analyze the structure of the population based on a Bayesian approach, detecting 21 subpopulations. Most of the subpopulations were in agreement with the clustering analysis. Conclusions The data generated by flow cytometry, ITS and SSR supported the hypothesis about the occurrence of homeologue recombination between A and B genomes, leading to discrepancies in the number of sets or portions from each parental genome. These phenomenons have been largely disregarded in the evolution of banana, as the “single-step domestication” hypothesis had long predominated. These findings will have an impact in future breeding approaches. Structure analysis enabled the efficient detection of ancestry of recently developed tetraploid hybrids by breeding programs, and for some triploids. However, for the main commercial subgroups, Structure appeared to be less efficient to detect the ancestry in diploid groups, possibly due to sampling restrictions. The possibility of inferring the membership among accessions to correct the effects of genetic structure opens possibilities for its use in marker-assisted selection by association mapping.

Polarimetry of the binary PDS 144

Aims. Our goal is to study the circumstellar environment associated with each component of the wide intermediate-mass pre-main sequence binary system PDS 144 using broadband polarimetry. Methods. We present near-infrared (NIR) linear polarimetric observations of PDS 144 gathered with the IAGPOL imaging polarimeter along with the CamIV infrared camera at the Observatorio do Pico dos Dias (OPD). In addition, we re-analyzed OPD archive optical polarization to separate the binary and estimate the interstellar polarization using foreground stars. Results. After discounting the interstellar component, we found that both stars of the binary system are intrinsically polarized. The polarization vectors at optical and NIR bands of both components are aligned with the local magnetic field and the jet axis. These findings indicate an interplay between the interstellar magnetic field and the formation of the binary system. We also found that the PDS 144N is less polarized than its southern companion in the optical. However, in the NIR PDS 144N is more polarized. Our polarization data can only be explained by high inclinations (i greater than or similar to 80 degrees) for the disks of both members. In particular, comparisons of our NIR data with young stellar objects disk models suggest predominantly small grains in the circumstellar environment of PDS 144N. In spite of the different grain types in each component, the infrared spectral indexes indicate a coeval system. We also found evidence of coplanarity between the disks.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.