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Biology subjects

Danner, L.

Publications and source records attributed to Danner, L..

2 recordsLinked to original sources

Nucleocytoviricota viral factories are transient organelles made by phase separation

Phase separation is a widespread mechanism in viral processes, mediating replication, host manipulation and virion morphogenesis. The phylum Nucleocytoviricota encompasses diverse and ubiquitous viruses, including Poxviridae, the climate-modulating Emiliania huxleyi virus and other so-called Giant Viruses. Cytoplasmic members of this phylum form viral factories but their nature has remained unresolved. Here, we demonstrate that these viral factories are formed by liquid-liquid phase separation. We prove that mimivirus viral factories are formed by multilayered phase separation, orchestrated by at least two scaffold proteins. To extend these findings across the phylum Nucleocytoviricota, we developed a bioinformatic pipeline to predict scaffold proteins based on a conserved molecular grammar, despite major primary sequence variability. Scaffold candidates were validated in Marseilleviridae and Poxviridae, highlighting a role of H5 as a scaffold protein in the vaccinia virus. Finally, we provide a repertoire of client proteins of the nucleus-like viral factory of mimivirus and demonstrate important sub-compartmentalization of functions, including those related to the central dogma. Overall, we reveal a new mechanism for an organelle to deploy nuclear-like functions entirely based on phase separation and re-classified phylum Nucleocytoviricota viral factories as biomolecular condensates.

microbiology↗

EpyNN: Educational python for Neural Networks

SummaryArtificial Neural Networks (ANNs) have achieved unequaled performance for numerous problems in many areas of Science, Business, Public Policy, and more. While experts are familiar with performance-oriented software and underlying theory, ANNs are difficult to comprehend for non-experts because it requires skills in programming, background in mathematics and knowledge of terminology and concepts. In this work, we release EpyNN, an educational python resource meant for a public willing to understand key concepts and practical implementation of scalable ANN architectures from concise, homogeneous and idiomatic source code. EpyNN contains an educational Application Programming Interface (API), educational workflows from data preparation to ANN training and a documentation website setting side-by-side code, mathematics, graphical representation and text to facilitate learning and provide teaching material. Overall, EpyNN provides basics for python-fluent individuals who wish to learn, teach or develop from scratch. AvailabilityEpyNN documentation is available at https://epynn.net and repository can be retrieved from https://github.com/synthaze/epynn. ContactStephanie Olivier-Van-Stichelen, solivier@mcw.edu. Supplementary InformationSupplementary files and listings.

scientific communication and education↗