Cryotomography of budding influenza a virus reveals filaments with diverse morphologies that mostly do not bear a genome at their distal end

Influenza viruses exhibit striking variations in particle morphology between strains. Clinical isolates of influenza A virus have been shown to produce long filamentous particles while laboratory-adapted strains are predominantly spherical. However, the role of the filamentous phenotype in the influenza virus infectious cycle remains undetermined. We used cryo-electron tomography to conduct the first three-dimensional study of filamentous virus ultrastructure in particles budding from infected cells. Filaments were often longer than 10 microns and sometimes had bulbous heads at their leading ends, some of which contained tubules we attribute to M1 while none had recognisable ribonucleoprotein (RNP) and hence genome segments. Long filaments that did not have bulbs were infrequently seen to bear an ordered complement of RNPs at their distal ends. Imaging of purified virus also revealed diverse filament morphologies; short rods (bacilliform virions) and longer filaments. Bacilliform virions contained an ordered complement of RNPs while longer filamentous particles were narrower and mostly appeared to lack this feature, but often contained fibrillar material along their entire length. The important ultrastructural differences between these diverse classes of particles raise the possibility of distinct morphogenetic pathways and functions during the infectious process

Examining the Auroral Ionosphere in Three Dimensions Using Reconstructed 2D Maps of Auroral Data to Drive the 3D GEMINI Model

We use the Geospace Environment Model of Ion-Neutral Interactions (GEMINI) to create three-dimensional, time-dependent simulations of auroral ionospheric parameters in the localized, several 100 km region surrounding auroral arcs observed during a winter 2017 sounding rocket campaign, resolving three-dimensional features of fine-scale (km) flow structures in the vicinity of an auroral arc. The three-dimensional calculations of GEMINI allow (with sufficient driving data) auroral current closure to be investigated without idealizing assumptions of sheet-like structures or height integrated ionospheres. Datamaps for two nearly sheet-like arcs are reconstructed from replications of the Isinglass sounding rocket campaign data, and combined with camera-based particle inversions into a set of driving inputs to run the 3D time-dependent model. Comparisons of model results to radar density profiles and to in situ magnetometry observations are presented. Slices of volumetric current, flow, and conductance structures from model outputs are used to interpret closure currents in an auroral arc region, and are compared to original in situ measurements for verification. The predominant source of return current region field aligned current closure for these slow time variation events is seen to be from the conductance gradients, including the Hall. The importance of the versus terms in the determination of the current structure provides a more complicated picture than a previous GEMINI study, which relied predominantly on the divergence of the electric field to determine current structure. Sensitivity of data-driven model results to details of replication and reconstruction processes are discussed, with improvements outlined for future work

Multiwavelength observations of the EUV variable metal-rich white dwarf GD 394

We present new Hubble Space Telescope (HST) ultraviolet and ground-based optical observations of the hot, metal-rich white dwarf GD394. Extreme-ultraviolet (EUV) observations in 1992-1996 revealed a 1.15 d periodicity with a 25 per cent amplitude, hypothesized to be due to metals in a surface accretion spot. We obtained phase resolved HST/Space Telescope Imaging Spectrograph high resolution far-ultraviolet spectra of GD394 that sample the entire period, along with a large body of supplementary data. We find no evidence for an accretion spot, with the flux, accretion rate, and radial velocity of GD394 constant over the observed time-scales at ultraviolet and optical wavelengths. We speculate that the spot may have no longer been present when our observations were obtained, or that the EUV variability is being caused by an otherwise undetected evaporating planet. The atmospheric parameters obtained from separate fits to optical and ultraviolet spectra are inconsistent, as is found for multiple hot white dwarfs. We also detect non-photospheric, high ionisation absorption lines of multiple volatile elements, which could be evidence for a hot plasma cocoon surrounding the white dwarf.European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013 / ERC Grant) [320964]; Leverhulme Trust Research Project Grant; NASA [NAS 5-26555]; NASA through a Space Telescope Science Institute [13719]; W. M. Keck FoundationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

NGTS clusters survey -- II. White-light flares from the youngest stars in Orion

We present the detection of high energy white-light flares from pre-main sequence stars associated with the Orion complex, observed as part of the Next Generation Transit Survey (NGTS). With energies up to $5.2\times10^{35}$ erg these flares are some of the most energetic white-light flare events seen to date. We have used the NGTS observations of flaring and non-flaring stars to measure the average flare occurrence rate for 4 Myr M0-M3 stars. We have also combined our results with those from previous studies to predict average rates for flares above $1\times10^{35}$ ergs for early M stars in nearby young associations.STFC ST/M001962/1; ST/P000495/

The Trypanosoma cruzi Virulence Factor Oligopeptidase B (OPBTc) Assembles into an Active and Stable Dimer

Oligopeptidase B, a processing enzyme of the prolyl oligopeptidase family, is considered as an important virulence factor in trypanosomiasis. Trypanosoma cruzi oligopeptidase B (OPBTc) is involved in host cell invasion by generating a Ca2+-agonist necessary for recruitment and fusion of host lysosomes at the site of parasite attachment. The underlying mechanism remains unknown and further structural and functional characterization of OPBTc may help clarify its physiological function and lead to the development of new therapeutic molecules to treat Chagas disease. In the present work, size exclusion chromatography and analytical ultracentrifugation experiments demonstrate that OPBTc is a dimer in solution, an association salt and pH-resistant and independent of intermolecular disulfide bonds. The enzyme retains its dimeric structure and is fully active up to 42°C. OPBTc is inactivated and its tertiary, but not secondary, structure is disrupted at higher temperatures, as monitored by circular dichroism and fluorescence spectroscopy. It has a highly stable secondary structure over a broad range of pH, undergoes subtle tertiary structure changes at low pH and is less stable under moderate ionic strength conditions. These results bring new insights into the structural properties of OPBTc, contributing to future studies on the rational design of OPBTc inhibitors as a promising strategy for Chagas disease chemotherapy

Automatic vetting of planet candidates from ground based surveys : machine learning with NGTS

State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context