Complete uniparental chromosome elimination occurs in several interspecific hybrids of plants. We studied the mechanisms underlying selective elimination of the paternal chromosomes during the development of wheat (Triticum aestivum) 3 pearl millet (Pennisetum glaucum) hybrid embryos. All pearl millet chromosomes were eliminated in a random sequence between 6 and 23 d after pollination. Parental genomes were spatially separated within the hybrid nucleus, and pearl millet chromatin destined for elimination occupied peripheral interphase positions. Structural reorganization of the paternal chromosomes occurred, and mitotic behavior differed between the parental chromosomes. We provide evidence for a novel chromosome elimination pathway that involves the formation of nuclear extrusions during interphase in addition to postmitotically formed micronuclei. The chromatin structure of nuclei and micronuclei is different, and heterochromatinization and DNA fragmentation of micronucleated pearl millet chromatin is the final step during haploidization.

There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases.

SummaryAnalysis of gene expression in the developing barley caryopsis requires effective instruments for visualization of the grain and the 3D expression patterns. Digital models of developing barley (Hordeum vulgare) grains were reconstructed from serial sections to visualize the complex three-dimensional (3D) grain anatomy, to generate and analyse 3D expression patterns, and to quantify tissues during growth. The models provide detailed spatial descriptions of developing grains at anthesis, at the syncytial stage of endosperm development and at the onset of starch accumulation, visualizing and quantifying 18 tissues or tissue complexes. Total caryopsis volumes and volume changes of specific tissues between the stages were determined, and proportions of ovule-and non-ovule-tissues and ratios of filial to maternal tissues were calculated from the model data. To generate and analyse 3D expression patterns, data from mRNA localization by in situ hybridizations were integrated into the models. At the onset of starch accumulation, cell-wall invertase (HvCWINV1) mRNA is mainly localized in the transfer cells and to a lesser degree in zones of the starchy endosperm. Using the model, an expression gradient across the grain was visualized. The expression pattern in the upper region of the caryopsis resembles that found in the median region at an earlier stage, indicating the presence of a developmental gradient. At anthesis, mRNA of the protease nucellin was visualized in a distinct zone of the nucellus near the antipodal cells.

No abstract

Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks. Current approaches including shallow embeddings and Graph Neural Networks have mostly been tested with node classification and link prediction tasks. In this work, we provide an application oriented perspective to a set of popular embedding approaches and evaluate their representational power with respect to realworld graph properties. We implement an extensive empirical data-driven framework to challenge existing norms regarding the expressive power of embedding approaches in graphs with varying patterns along with a theoretical analysis of the limitations we discovered in this process. Our results suggest that "one-to-fit-all" GRL approaches are hard to define in real-world scenarios and as new methods are being introduced they should be explicit about their ability to capture graph properties and their applicability in datasets with non-trivial structural differences.