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Abstract Optical surveys, such as the MACHO project, often uncover variable stars whose classification requires follow‐up observations by other instruments. We performed X‐ray spectroscopy and photometry of the unusual variable star MACHO 311.37557.169 with
XMM‐Newton in April 2018, supplemented by archival X‐ray and optical spectrographic data. The star has a bolometric X‐ray luminosity of about 1 × 1032 erg s−1 cm−2and a heavily absorbed two‐temperature plasma spectrum. The shape of its light curve, its overall brightness, its X‐ray spectrum, and the emission lines in its optical spectrum suggest that it is most likely a VY Scl cataclysmic variable. -
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Abstract We describe sloth assemblages from the
C ocinetasB asin (L aG uajira peninsula,C olombia), found in theN eogeneC astilletes andW are formations, located in northernmostS outhA merica, documenting otherwise poorly known biotas. The tentative referral of a specimen to a small megatherioid sloth,H yperleptusM ioceneC astilletesF ormation, suggests affinities of this fauna with the distantS antaC ruzF ormation and documents a large latitudinal distribution for this taxon. The lateP lioceneW areF ormation is much more diverse, with five distinct taxa representing every family of ‘ground sloths’. This diversity is also remarkable at the ecological level, with sloths spanning over two orders of magnitude of body mass and probably having different feeding strategies. Being only a few hundred kilometres away from theI sthmus ofP anama, and a few hundred thousand years older than the classically recognized first main pulse of theG reatA mericanB iotic interchange (GABI 1), theW areF ormation furthermore documents an important fauna for the understanding of this major event inN eogene palaeobiogeography. The sloths for which unambiguous affinities were recovered are not closely related to the early immigrants found inN orthA merica beforeGABI 1. -
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Abstract The recent breakthroughs in structure prediction, where methods such as AlphaFold demonstrated near‐atomic accuracy, herald a paradigm shift in structural biology. The 200 million high‐accuracy models released in the AlphaFold Database are expected to guide protein science in the coming decades. Partitioning these AlphaFold models into domains and assigning them to an evolutionary hierarchy provide an efficient way to gain functional insights into proteins. However, classifying such a large number of predicted structures challenges the infrastructure of current structure classifications, including our Evolutionary Classification of protein Domains (ECOD). Better computational tools are urgently needed to parse and classify domains from AlphaFold models automatically. Here we present a Domain Parser for AlphaFold Models (DPAM) that can automatically recognize globular domains from these models based on inter‐residue distances in 3D structures, predicted aligned errors, and ECOD domains found by sequence (HHsuite) and structural (Dali) similarity searches. Based on a benchmark of 18,759 AlphaFold models, we demonstrate that DPAM can recognize 98.8% of domains and assign correct boundaries for 87.5%, significantly outperforming structure‐based domain parsers and homology‐based domain assignment using ECOD domains found by HHsuite or Dali. Application of DPAM to the massive AlphaFold models will enable efficient classification of domains, providing evolutionary contexts and facilitating functional studies.
