Extreme Ultraviolet Quasar Colours from GALEX Observations of the SDSS DR14Q Catalogue

The rest-frame far to extreme ultraviolet (UV) colour–redshift relationship has been constructed from data on over 480,000 quasars carefully cross-matched between SDSS Data Release 14 and the final GALEX photometric catalogue. UV matching and detection probabilities are given for all the quasars, including dependencies on separation, optical brightness, and redshift. Detection limits are also provided for all objects. The UV colour distributions are skewed redward at virtually all redshifts, especially when detection limits are accounted for. The median GALEX far-UV minus near-UV (FUV − NUV) colour–redshift relation is reliably determined up to z ≈ 2.8, corresponding to rest-frame wavelengths as short as 400 Å. Extreme UV (EUV) colours are substantially redder than found previously, when detection limits are properly accounted for. Quasar template spectra were forward modelled through the GALEX bandpasses, accounting for intergalactic opacity, intrinsic reddening, and continuum slope variations. Intergalactic absorption by itself cannot account for the very red EUV colours. The colour–redshift relation is consistent with no intrinsic reddening, at least for SMC-like extinction. The best model fit has a FUV continuum power-law slope αν, FUV = −0.34 ± 0.03 consistent with previous results, but an EUV slope αν, EUV = −2.90 ± 0.04 that is much redder and inconsistent with any previous composite value (all ≳ −2.0). The EUV slope difference can be attributed in part to the tendency of previous studies to preferentially select UV brighter and bluer objects. The weak EUV flux suggests quasar accretion disc models that include outflows such as disc winds

Low-Cost Access to the Deep, High-Cadence Sky: the Argus Optical Array

New mass-produced, wide-field, small-aperture telescopes have the potential to revolutionize ground-based astronomy by greatly reducing the cost of collecting area. In this paper, we introduce a new class of large telescope based on these advances: an all-sky, arcsecond-resolution, 1000-telescope array which builds a simultaneously high-cadence and deep survey by observing the entire sky all night. As a concrete example, we describe the Argus Array, a 5m-class telescope with an all-sky field of view and the ability to reach extremely high cadences using low-noise CMOS detectors. Each 55 GPix Argus exposure covers 20% of the entire sky to g=19.6 each minute and g=21.9 each hour; a high-speed mode will allow sub-second survey cadences for short times. Deep coadds will reach g=23.6 every five nights over 47% of the sky; a larger-aperture array telescope, with an \'etendue close to the Rubin Observatory, could reach g=24.3 in five nights. These arrays can build two-color, million-epoch movies of the sky, enabling sensitive and rapid searches for high-speed transients, fast-radio-burst counterparts, gravitational-wave counterparts, exoplanet microlensing events, occultations by distant solar system bodies, and myriad other phenomena. An array of O(1,000) telescopes, however, would be one of the most complex astronomical instruments yet built. Standard arrays with hundreds of tracking mounts entail thousands of moving parts and exposed optics, and maintenance costs would rapidly outpace the mass-produced-hardware cost savings compared to a monolithic large telescope. We discuss how to greatly reduce operations costs by placing all optics in a thermally controlled, sealed dome with a single moving part. Coupled with careful software scope control and use of existing pipelines, we show that the Argus Array could become the deepest and fastest Northern sky survey, with total costs below $20M.Comment: 17 pages, 5 figures, 2 table

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

SHIV89.6P pathogencity in cynomolgus monkeys and control of viral replication and disease onset by human immunodeficiency virus type 1 Tat vaccine

The Tat protein of human immunodeficiency virus (HIV) is produced very early after infection, plays a key role in the virus life cycle and in acquired immunodeficiency syndrome (AIDS) pathogenesis, is immunogenic and well conserved among all virus clades. Notably, a Tat-specific immune response correlates with non-progression to AIDS. Here, we show that a vaccine based on the Tat protein of HIV blocks primary infection with the simian/human immunodeficiency virus (SHIV)89.6P and prevents the CD4 T cell decline and disease onset in cynomolgus monkeys. No signs of virus replication were found in five out of seven vaccinated macaques for almost 1 year of follow-up. Since the inoculated virus (derived from rhesus or from cynomolgus macaques) is shown to be highly pathogenic in cynomolgus macaques, the results indicate efficacy of Tat vaccination in protection against highly pathogenic virus challenge. Finally, the studies of the Tat-specific immunological responses indicate a correlation of protection with a cytotoxic T cell response. Thus, a Tat-based vaccine is a promising candidate for preventive and therapeutic vaccination in humans

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships