The Diversification of the LIM Superclass at the Base of the Metazoa Increased Subcellular Complexity and Promoted Multicellular Specialization

Background: Throughout evolution, the LIM domain has been deployed in many different domain configurations, which has led to the formation of a large and distinct group of proteins. LIM proteins are involved in relaying stimuli received at the cell surface to the nucleus in order to regulate cell structure, motility, and division. Despite their fundamental roles in cellular processes and human disease, little is known about the evolution of the LIM superclass. Results: We have identified and characterized all known LIM domain-containing proteins in six metazoans and three nonmetazoans. In addition, we performed a phylogenetic analysis on all LIM domains and, in the process, have identified a number of novel non-LIM domains and motifs in each of these proteins. Based on these results, we have formalized a classification system for LIM proteins, provided reasonable timing for class and family origin events; and identified lineagespecific loss events. Our analysis is the first detailed description of the full set of LIM proteins from the non-bilaterian species examined in this study. Conclusion: Six of the 14 LIM classes originated in the stem lineage of the Metazoa. The expansion of the LIM superclass at the base of the Metazoa undoubtedly contributed to the increase in subcellular complexity required for the transition from a unicellular to multicellular lifestyle and, as such, was a critically important event in the history of animal multicellularity

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p