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Background: Low back pain is highly prevalent and the main cause of years lived with disability (YLDs). We present the most up-to-date global, regional, and national data on prevalence and YLDs for low back pain from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021. Methods: Population-based studies from 1980 to 2019 identified in a systematic review, international surveys, US medical claims data, and dataset contributions by collaborators were used to estimate the prevalence and YLDs for low back pain from 1990 to 2020, for 204 countries and territories. Low back pain was defined as pain between the 12th ribs and the gluteal folds that lasted a day or more; input data using alternative definitions were adjusted in a network meta-regression analysis. Nested Bayesian meta-regression models were used to estimate prevalence and YLDs by age, sex, year, and location. Prevalence was projected to 2050 by running a regression on prevalence rates using Socio-demographic Index as a predictor, then multiplying them by projected population estimates. Findings: In 2020, low back pain affected 619 million (95% uncertainty interval 554–694) people globally, with a projection of 843 million (759–933) prevalent cases by 2050. In 2020, the global age-standardised rate of YLDs was 832 per 100 000 (578–1070). Between 1990 and 2020, age-standardised rates of prevalence and YLDs decreased by 10·4% (10·9–10·0) and 10·5% (11·1–10·0), respectively. A total of 38·8% (28·7–47·0) of YLDs were attributed to occupational factors, smoking, and high BMI. Interpretation: Low back pain remains the leading cause of YLDs globally, and in 2020, there were more than half a billion prevalent cases of low back pain worldwide. While age-standardised rates have decreased modestly over the past three decades, it is projected that globally in 2050, more than 800 million people will have low back pain. Challenges persist in obtaining primary country-level data on low back pain, and there is an urgent need for more high-quality, primary, country-level data on both prevalence and severity distributions to improve accuracy and monitor change. Funding: Bill and Melinda Gates Foundation.

M. Dixon et al., We use stacked spectra of the host galaxies of photometrically identified Type Ia supernovae (SNe Ia) from the Dark Energy Survey (DES) to search for correlations between Hubble diagram residuals and the spectral properties of the host galaxies. Utilizing full spectrum fitting techniques on stacked spectra binned by Hubble residual, we find no evidence for trends between Hubble residuals and properties of the host galaxies that rely on spectral absorption features (, MD would like to acknowledge support through an Australian Government Research Training Program Scholarship. This research was supported by the Australian Research Council The Centre of Excellence for Dark Matter Particle Physics (CDM; project number CE200100008) and the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav; project number CE170100004). This project/publication was made possible through the support of a grant from the John Templeton Foundation. The authors gratefully acknowledge this grant ID 61807, Two Standard Models Meet. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NSF’s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. Based on data acquired at the Anglo-Australian Telescope, under program A/2013B/012. We acknowledge the traditional custodians of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present.

Schizophrenia is a mental illness that can disrupt emotions, perceptions, and cognition and reduce quality of life. The classical approach to treat schizophrenia is to use typical and atypical antipsychotics; however, limitations include low efficacy in mitigating negative symptoms and cognitive dysfunctions and a range of adverse effects. Evidence has accumulated on trace amine-associated receptor 1 (TAAR1) as a novel therapeutic target for treating schizophrenia. This systematic review investigates the available evidence on a TAAR1 agonist, ulotaront, as a treatment for schizophrenia. A systematic search was conducted on PubMed/MEDLINE and Ovid databases for English-published articles from inception to 18 December 2022. The literature focusing on the association between ulotaront and schizophrenia was evaluated based on an inclusion/exclusion criterion. Selected studies were assessed for the risk of bias, using the Cochrane Collaboration tool, and summarized in a table to generate discussion topics. Three clinical, two comparative, and five preclinical studies examining ulotaront’s pharmacology, tolerability and safety, and/or efficacy were identified. Results indicate that ulotaront has a differing adverse effect profile from other antipsychotics, may mitigate metabolic-related adverse effects commonly associated with antipsychotics, and may be effective for treating positive and negative symptoms. Findings from the available literature present ulotaront as a potential and promising alternative treatment method for schizophrenia. Despite this, our results were limited due to the lack of clinical trials on ulotaront’s long-term efficacy and mechanisms of action. Future research should focus on these limitations to elucidate ulotaront’s efficacy and safety for the treatment of schizophrenia and other mental disorders with similar pathophysiology.

We introduce a new software package for modeling the point-spread function (PSF) of astronomical images, called Piff (PSFs In the Full FOV), which we apply to the first three years (known as Y3) of the Dark Energy Survey (DES) data. We describe the relevant details about the algorithms used by Piff to model the PSF, including how the PSF model varies across the field of view (FOV). Diagnostic results show that the systematic errors from the PSF modeling are very small over the range of scales that are important for the DES Y3 weak lensing analysis. In particular, the systematic errors from the PSF modeling are significantly smaller than the corresponding results from the DES year one (Y1) analysis. We also briefly describe some planned improvements to Piff that we expect to further reduce the modeling errors in future analyses., Comment: 19 pages, accepted/published by MNRAS. See https://www.darkenergysurvey.org/des-year-3-cosmology-results-papers/ for the full DES Y3 cosmology release

We present a simulated cosmology analysis using the second and third moments of the weak lensing mass (convergence) maps. The second moment, or variances, of the convergence as a function of smoothing scale contains information similar to standard shear two-point statistics. The third moment, or the skewness, contains additional non-Gaussian information. The analysis is geared towards the third year (Y3) data from the Dark Energy Survey (DES), but the methodology can be applied to other weak lensing data sets. We present the formalism for obtaining the convergence maps from the measured shear and for obtaining the second and third moments of these maps given partial sky coverage. We estimate the covariance matrix from a large suite of numerical simulations. We test our pipeline through a simulated likelihood analyses varying 5 cosmological parameters and 10 nuisance parameters and identify the scales where systematic or modelling uncertainties are not expected to affect the cosmological analysis. Our simulated likelihood analysis shows that the combination of second and third moments provides a 1.5 per cent constraint on S8 σ8(ωm/0.3)0.5 for DES Year 3 data. This is 20 per cent better than an analysis using a simulated DES Y3 shear two-point statistics, owing to the non-Gaussian information captured by the inclusion of higher order statistics. This paper validates our methodology for constraining cosmology with DES Year 3 data, which will be presented in a subsequent paper., Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundac¸ao Carlos Chagas Filho de Amparo ˜ a Pesquisa do ` Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cient´ıfico e Tecnologico and the Minist ´ erio da Ci ´ encia, Tecnologia ˆ e Inovac¸ao, the Deutsche Forschungsgemeinschaft and the Collabo- ˜ rating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California, Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y ´ Tecnologicas-Madrid, the University of Chicago, University College ´ London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Z ¨ urich, Fermi ¨ National Accelerator Laboratory, the University of Illinois at UrbanaChampaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the ` Institut de F´ısica d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universitat M¨ unchen, and the ¨ associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015- 71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union 7th Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) e-Universe (CNPq grant ˆ 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

Protein misfolding is a recurring phenomenon that cells must manage; otherwise misfolded proteins can aggregate and become toxic should they persist. To counter this burden, cells have evolved protein quality control (PQC) mechanisms that manage misfolded proteins. Two classes of systems that function in PQC are chaperones that aid in protein folding and ubiquitin–protein ligases that ubiquitinate misfolded proteins for proteasomal degradation. How folding and degradative PQC systems interact and coordinate their respective functions is not yet fully understood. Previous studies of PQC degradation pathways in the endoplasmic reticulum and cytosol have led to the prevailing idea that these pathways require the activity of Hsp70 chaperones. Here, we find that involvement of the budding yeast Hsp70 chaperones Ssa1 and Ssa2 in nuclear PQC degradation varies with the substrate. In particular, nuclear PQC degradation mediated by the yeast ubiquitin–protein ligase San1 often involves Ssa1/Ssa2, but San1 substrate recognition and ubiquitination can proceed without these Hsp70 chaperone functions in vivo and in vitro. Our studies provide new insights into the variability of Hsp70 chaperone involvement with a nuclear PQC degradation pathway.

DES Collaboration: N. MacCrann et al., As the statistical power of galaxy weak lensing reaches per cent level precision, large, realistic, and robust simulations are required to calibrate observational systematics, especially given the increased importance of object blending as survey depths increase. To capture the coupled effects of blending in both shear and photometric redshift calibration, we define the effective redshift distribution for lensing, nγ(z), and describe how to estimate it using image simulations. We use an extensive suite of tailored image simulations to characterize the performance of the shear estimation pipeline applied to the Dark Energy Survey (DES) Year 3 data set. We describe the multiband, multi-epoch simulations, and demonstrate their high level of realism through comparisons to the real DES data. We isolate the effects that generate shear calibration biases by running variations on our fiducial simulation, and find that blending-related effects are the dominant contribution to the mean multiplicative bias of approximately −2 per cent ⁠. By generating simulations with input shear signals that vary with redshift, we calibrate biases in our estimation of the effective redshift distribution, and demonstrate the importance of this approach when blending is present. We provide corrected effective redshift distributions that incorporate statistical and systematic uncertainties, ready for use in DES Year 3 weak lensing analyses., Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NFS’s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. NM acknowledges support from a European Research Council (ERC) Starting Grant under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 851274). MRB is supported by DOE grant DE-AC02-06CH11357. This work was supported by the Department of Energy, Laboratory Directed Research and Development program at SLAC National Accelerator Laboratory, under contract DE-AC02-76SF00515 and as part of the Panofsky Fellowship awarded to DG. MJ is partially supported by the U.S. Department of Energy grant DE-SC0007901 and funds from the University of Pennsylvania. AC acknowledges support from NASA grant 15-WFIRST15-0008. RR, IH, and SB acknowledge support from the European Research Council in the form of a Consolidator Grant with number 681431. IH also acknowledges support from the Beecroft Trust. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory, and the RHIC Atlas Computing Facility, operated by Brookhaven National Laboratory. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

Pedro H. Bernardinelli et al., We present a search for outer solar system objects in the 6 yr of data from the Dark Energy Survey (DES). The DES covered a contiguous 5000 deg2 of the southern sky with ≈80,000 3 deg2 exposures in the grizY filters between 2013 and 2019. This search yielded 812 trans-Neptunian objects (TNOs), one Centaur and one Oort cloud comet, 458 reported here for the first time. We present methodology that builds upon our previous search on the first 4 yr of data. All images were reprocessed with an optimized detection pipeline that leads to an average completeness gain of 0.47 mag per exposure, as well as improved transient catalog production and algorithms for linkage of detections into orbits. All objects were verified by visual inspection and by the "sub-threshold significance," the signal-to-noise ratio in the stack of images in which its presence is indicated by the orbit, but no detection was reported. This yields a pure catalog complete to r ≈ 23.8 mag and distances 29 < d < 2500 au. The TNOs have minimum (median) of 7 (12) nights' detections and arcs of 1.1 (4.2) yr, and will have grizY magnitudes available in a further publication. We present software for simulating our observational biases for comparisons of models to our detections. Initial inferences demonstrating the catalog's statistical power are: the data are inconsistent with the CFEPS-L7 model for the classical Kuiper Belt; the 16 "extreme" TNOs (a > 150 au, q > 30 au) are consistent with the null hypothesis of azimuthal isotropy; and nonresonant TNOs with q > 38 au, a > 50 au show a significant tendency to be sunward of major mean-motion resonances., University of Pennsylvania authors have been supported in this work by grants AST-1515804 and AST-2009210 from the National Science Foundation, and grant DE-SC0007901 from the Department of Energy. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NSF's NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under grant Nos. AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007–2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2).

DES collaboration: et al., The DMASS sample is a photometric sample from the DES Year 1 data set designed to replicate the properties of the CMASS sample from BOSS, in support of a joint analysis of DES and BOSS beyond the small overlapping area. In this paper, we present the measurement of galaxy–galaxy lensing using the DMASS sample as gravitational lenses in the DES Y1 imaging data. We test a number of potential systematics that can bias the galaxy–galaxy lensing signal, including those from shear estimation, photometric redshifts, and observing conditions. After careful systematic tests, we obtain a highly significant detection of the galaxy–galaxy lensing signal, with total S/N = 25.7. With the measured signal, we assess the feasibility of using DMASS as gravitational lenses equivalent to CMASS, by estimating the galaxy-matter cross-correlation coefficient rcc. By jointly fitting the galaxy–galaxy lensing measurement with the galaxy clustering measurement from CMASS, we obtain rcc=1.09+0.12−0.11 for the scale cut of 4h−1Mpc and rcc=1.06+0.13−0.12 for 12h−1Mpc in fixed cosmology. By adding the angular galaxy clustering of DMASS, we obtain rcc = 1.06 ± 0.10 for the scale cut of 4h−1Mpc and rcc = 1.03 ± 0.11 for 12h−1Mpc⁠. The resulting values of rcc indicate that the lensing signal of DMASS is statistically consistent with the one that would have been measured if CMASS had populated the DES region within the given statistical uncertainty. The measurement of galaxy–galaxy lensing presented in this paper will serve as part of the data vector for the forthcoming cosmology analysis in preparation., AC acknowledges support from NASA grant 15-WFIRST15-0008. During the preparation of this paper, C.H. was supported by the Simons Foundation, NASA, and the U.S. Department of Energy. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NFS’s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. This work used resources at the Owens Cluster at the Ohio Supercomputer Center (OSC 1987) and the Duke Compute Cluster (DCC) at Duke University.

To explain the factors contributing to the gap in depression between employed arthritis patients with and without paid sick leave.Blinder-Oaxaca decomposition analysis was used to identify factors that explain the gap in the experience of depressive symptoms among arthritis patients with paid and unpaid sick leave. Data from the 2018 National Health Interview Survey, USA, was used.A total of 7189 of the NHIS survey participants given the diagnosis of arthritis were identified, of which 39% were male and 61% were female, with mean age of 63.5 years. The decomposition findings suggest patients in the unpaid sick leave group were more likely to report depressive symptoms compared to patients with paid sick leave. The major contributors to the gap in the report of depressive symptoms are sex (female) and annual income (less than 35,000 USD).Findings suggest that the absence of paid sick leave is a key determinant for experiencing depressive symptoms among individuals with arthritis. The provision of paid sick leave may reduce report of depressive symptoms among employed arthritis patients in the USA.• Individuals with arthritis are consistently at greater risk of depression and unemployment as compared to individuals without arthritis. • To date greater emphasis is put on determinants of unemployment, while there is no available data on benefits associated with being employed, such as sick leave, and how it affects mental health. • Patients with unpaid sick leave appear to experience more persistent depressive symptoms than patients with access to paid sick leave. • To tackle burden of depression among arthritis patients, provision of paid sick leave may be an effective intervention.

The paper ‘Is every strong lens model unhappy in its own way? Uniform modelling of a sample of 13 quadruply+ imaged quasars’ was published in MNRAS, 483, 4, 5649–5671 (2019). The coordinate values of the image positions in table 4 were wrongly printed due to a clerical error. At a later stage of writing the manuscript, we have changed the zero-point definition of the lens coordinate systems, but the relative image positions were not accounted for this change of definition while printing out table 4. We provide the updated Table 4 below. This error does not impact any other results of the paper in any way, except for the table itself. We thank Collin Werner and Paul Schechter for helping us identify this error.

Lemos, P. et al., Quantifying tensions – inconsistencies amongst measurements of cosmological parameters by different experiments – has emerged as a crucial part of modern cosmological data analysis. Statistically significant tensions between two experiments or cosmological probes may indicate new physics extending beyond the standard cosmological model and need to be promptly identified. We apply several tension estimators proposed in the literature to the dark energy survey (DES) large-scale structure measurement and Planck cosmic microwave background data. We first evaluate the responsiveness of these metrics to an input tension artificially introduced between the two, using synthetic DES data. We then apply the metrics to the comparison of Planck and actual DES Year 1 data. We find that the parameter differences, Eigentension, and Suspiciousness metrics all yield similar results on both simulated and real data, while the Bayes ratio is inconsistent with the rest due to its dependence on the prior volume. Using these metrics, we calculate the tension between DES Year 1 3 × 2pt and Planck, finding the surveys to be in ∼2.3σ tension under the ΔCDM paradigm. This suite of metrics provides a toolset for robustly testing tensions in the DES Year 3 data and beyond., The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by Ministerio de Ciencia e Innovación (MICINN) under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the Centres de Recerca de Catalunya (CERCA) program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

Resumen Antecedentes y objetivo La intoxicacion por hongos no es muy frecuente pero si potencialmente grave: su sintomatologia es ambigua y tardia, requiere de habilidad y conocimientos para identificar el agente causante, etc. Disponer de herramientas de consulta rapida y eficiente puede ser una ayuda valiosa en medios como urgencias y atencion primaria. Teniendo en cuenta la utilizacion generalizada de dispositivos moviles, el formato app se presenta como un diseno optimo. Hasta donde alcanza el conocimiento de los autores, existen pocas aplicaciones dedicadas a la toxicologia y menos a los micetismos. El objetivo del desarrollo de MicoApp es proporcionar una herramienta que facilite, sin sustituir su criterio clinico, el diagnostico clinico y de laboratorio de los facultativos ante una posible intoxicacion por hongos. Materiales y metodos MicoApp ha sido desarrollada en un entorno key responsive adaptable a ordenadores personales y dispositivos moviles (smartphones, tabletas...) para ser utilizada con facilidad, relacionando aspectos de toxicologia, medicina clinica, medicina de laboratorio y un diseno grafico optimizado. Resultados y conclusiones Es un producto de distribucion gratuita, orientado al paciente, que contempla las intoxicaciones mas frecuentes, los hongos mas representativos y que contextualiza los cuadros clinicos y resultados de laboratorio en esta problematica. El contenido de MicoApp puede ser traducido, ampliado o enmendado facilmente, si ello fuera necesario.

We present weak lensing (WL) mass constraints for a sample of massive galaxy clusters detected by the South Pole Telescope (SPT) via the Sunyaev-Zeldovich effect (SZE). We use $griz$ imaging data obtained from the Science Verification (SV) phase of the Dark Energy Survey (DES) to fit the WL shear signal of 33 clusters in the redshift range $0.25 \le z \le 0.8$ with NFW profiles and to constrain a four-parameter SPT mass-observable relation. To account for biases in WL masses, we introduce a WL mass to true mass scaling relation described by a mean bias and an intrinsic, log-normal scatter. We allow for correlated scatter within the WL and SZE mass-observable relations and use simulations to constrain priors on nuisance parameters related to bias and scatter from WL. We constrain the normalization of the $\zeta-M_{500}$ relation, $A_\mathrm{SZ}=12.0_{-6.7}^{+2.6}$ when using a prior on the mass slope $B_\mathrm{SZ}$ from the latest SPT cluster cosmology analysis. Without this prior, we recover $A_\mathrm{SZ}=10.8_{-5.2}^{+2.3}$ and $B_\mathrm{SZ}=1.30_{-0.44}^{+0.22}$. Results in both cases imply lower cluster masses than measured in previous work with and without WL, although the uncertainties are large. The WL derived value of $B_\mathrm{SZ}$ is $\approx 20\%$ lower than the value preferred by the most recent SPT cluster cosmology analysis. The method demonstrated in this work is designed to constrain cluster masses and cosmological parameters simultaneously and will form the basis for subsequent studies that employ the full SPT cluster sample together with the DES data., Comment: Revised version in response to referee report

Andrade-Oliveira, F., et al. (DES Collaboration), We perform an analysis in harmonic space of the Dark Energy Survey Year 1 Data (DES-Y1) galaxy clustering photometric data using products obtained for the real-space analysis. We test our pipeline with a suite of lognormal simulations, which are used to validate scale cuts in harmonic space as well as to provide a covariance matrix that takes into account the DES-Y1 mask. We then apply this pipeline to DES-Y1 data taking into account survey property maps derived for the real-space analysis. We compare with real-space DES-Y1 results obtained from a similar pipeline. We show that the harmonic space analysis we develop yields results that are compatible with the real-space analysis for the bias parameters. This verification paves the way to performing a harmonic space analysis for the upcoming DES-Y3 data., The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

Grandis, S., et al. (DES Colaboration), We perform a cross validation of the cluster catalogue selected by the red-sequence Matched-filter Probabilistic Percolation algorithm (redMaPPer) in Dark Energy Survey year 1 (DES-Y1) data by matching it with the Sunyaev-Zel'dovich effect (SZE) selected cluster catalogue from the South Pole Telescope SPT-SZ survey. Of the 1005 redMaPPer selected clusters with measured richness $\hat{\lambda }\gt 40$ in the joint footprint, 207 are confirmed by SPT-SZ. Using the mass information from the SZE signal, we calibrate the richness-mass relation using a Bayesian cluster population model. We find a mass trend λ ∝ MB consistent with a linear relation (B ∼1), no significant redshift evolution and an intrinsic scatter in richness of σλ = 0.22 ± 0.06. By considering two error models, we explore the impact of projection effects on the richness-mass modelling, confirming that such effects are not detectable at the current level of systematic uncertainties. At low richness SPT-SZ confirms fewer redMaPPer clusters than expected. We interpret this richness dependent deficit in confirmed systems as due to the increased presence at low richness of low-mass objects not correctly accounted for by our richness-mass scatter model, which we call contaminants. At a richness $\hat{\lambda }=40$, this population makes up ${\gt}12{{\ \rm per\ cent}}$ (97.5 percentile) of the total population. Extrapolating this to a measured richness $\hat{\lambda }=20$ yields ${\gt}22{{\ \rm per\ cent}}$ (97.5 percentile). With these contamination fractions, the predicted redMaPPer number counts in different plausible cosmologies are compatible with the measured abundance. The presence of such a population is also a plausible explanation for the different mass trends (B ∼0.75) obtained from mass calibration using purely optically selected clusters. The mean mass from stacked weak lensing (WL) measurements suggests that these low-mass contaminants are galaxy groups with masses ∼3-5 × 1013 M⊙ which are beyond the sensitivity of current SZE and X-ray surveys but a natural target for SPT-3G and eROSITA., The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).

T. N. Varga et al., We develop a novel data-driven method for generating synthetic optical observations of galaxy clusters. In cluster weak lensing, the interplay between analysis choices and systematic effects related to source galaxy selection, shape measurement, and photometric redshift estimation can be best characterized in end-to-end tests going from mock observations to recovered cluster masses. To create such test scenarios, we measure and model the photometric properties of galaxy clusters and their sky environments from the Dark Energy Survey Year 3 (DES Y3) data in two bins of cluster richness λ∈[30;45) ⁠, λ∈[45;60) and three bins in cluster redshift (⁠z∈[0.3;0.35) ⁠, z∈[0.45;0.5) and z∈[0.6;0.65) ⁠. Using deep-field imaging data, we extrapolate galaxy populations beyond the limiting magnitude of DES Y3 and calculate the properties of cluster member galaxies via statistical background subtraction. We construct mock galaxy clusters as random draws from a distribution function, and render mock clusters and line-of-sight catalogues into synthetic images in the same format as actual survey observations. Synthetic galaxy clusters are generated from real observational data, and thus are independent from the assumptions inherent to cosmological simulations. The recipe can be straightforwardly modified to incorporate extra information, and correct for survey incompleteness. New realizations of synthetic clusters can be created at minimal cost, which will allow future analyses to generate the large number of images needed to characterize systematic uncertainties in cluster mass measurements., This research was supported by the Excellence Cluster ORIGINS which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strat- egy –EXC-2094-390783311. The calculations have been in part carried out on the computing facilities of the Computational Center for Particle and Astrophysics (C2PAP). This w ork w as supported by the Department of Energy, Laboratory Directed Research and Development program at SLAC National Accelerator Laboratory, under contract DE-AC02-76SF00515 and as part of the P anofsk y Fellowship awarded to DG. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomput- ing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Te xas A&M Univ ersity, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the Ministerio da Ciencia, Tecnologia e Inovacao, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Zurich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana- Champaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the Institut de Fisica d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universit ¨at M ¨unchen and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory , Stanford University , the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. The DES data management system is supported by the Na- tional Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV -2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020.

Cheng, Ting-Yun, et al., We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies (16 ≤ i < 18) at low redshift (z < 0.25), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes 16 ≤ i < 21, and redshifts z < 1.0, and provides predicted probabilities to two galaxy types – ellipticals and spirals (disc galaxies). Our CNN classifications reveal an accuracy of over 99 per cent for bright galaxies when comparing with the GZ1 classifications (i < 18). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorizes discy galaxies with rounder and blurred features, which humans often incorrectly visually classify as ellipticals. As a part of the validation, we carry out one of the largest examinations of non-parametric methods, including ∼100 ,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between ellipticals and spirals for this data set., The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015- 71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and the Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).

Cosmic voids gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint on degree scales. We use the simulated CMB lensing convergence map from the Marenostrum Institut de Ciencias de l’Espai (MICE) N-body simulation to calibrate our detection strategy for a given void definition and galaxy tracer density. We then identify cosmic voids in Dark Energy Survey (DES) Year 1 data and stack the Planck 2015 lensing convergence map on their locations, probing the consistency of simulated and observed void lensing signals. When fixing the shape of the stacked convergence profile to that calibrated from simulations, we find imprints at the 3σ significance level for various analysis choices. The best measurement strategies based on the MICE calibration process yield S/N ≈ 4 for DES Y1, and the best-fitting amplitude recovered from the data is consistent with expectations from MICE (A ≈ 1). Given these results as well as the agreement between them and N-body simulations, we conclude that the previously reported excess integrated Sachs–Wolfe (ISW) signal associated with cosmic voids in DES Y1 has no counterpart in the Planck CMB lensing map., This work has made use of CosmoHub (see Carretero et al. 2017). CosmoHub has been developed by the Port d’Informacio Cient ´ ´ıfica (PIC), maintained through a collaboration of the Institut de F´ısica d’Altes Energies (IFAE) and the Centro de Investigaciones Energeticas, Medioambientales y Tecnol ´ ogicas (CIEMAT), and was ´ partially funded by the ‘Plan Estatal de Investigacion Cient ´ ´ıfica y Tecnica y de Innovaci ´ on’ program of the Spanish government. ´ Funding for the DES Projects has been provided by the US Department of Energy, the US National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundac¸ao Carlos Chagas Filho de Amparo ˜ a Pesquisa do ` Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cient´ıfico e Tecnologico and the Minist ´ erio da Ci ´ encia, Tecnologia ˆ e Inovac¸ao, the Deutsche Forschungsgemeinschaft, and the Collab- ˜ orating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambien- ´ tales y Tecnologicas-Madrid, the University of Chicago, Univer- ´ sity College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) ¨ Zurich, Fermi National Accelerator Laboratory, the University of ¨ Illinois at Urbana-Champaign, the Institut de Ciencies de l’Espai ` (IEEC/CSIC), the Institut de F´ısica d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universitat¨ Munchen and the associated Excellence Cluster Universe, the Uni- ¨ versity of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. This paper is based in part on observations at Cerro Tololo InterAmerican Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015- 71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, 306478, and 615929. We acknowledge support from the Brazilian Instituto Nacional de Cienciae Tecnologia ˆ (INCT) e-Universe (CNPq grant 465376/2014-2). This paper has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the US Department of Energy, Office of Science, Office of High Energy Physics. PV acknowledges the support from the grant MIUR PRIN 2015 ‘Cosmology and Fundamental Physics: illuminating the Dark Universe with Euclid’. AK has been supported by a Juan de la Cierva fellowship from MINECO with project number IJC2018-037730-I. Funding for this project was also available in part through SEV-2015-0548 and AYA2017-89891-P. This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 754558.

Yu, Zhefu, et al., Reverberation mapping is a robust method to measure the masses of supermassive black holes outside of the local Universe. Measurements of the radius–luminosity (R−L) relation using the Mg II emission line are critical for determining these masses near the peak of quasar activity at z ≈ 1−2, and for calibrating secondary mass estimators based on Mg II that can be applied to large samples with only single-epoch spectroscopy. We present the first nine Mg II lags from our 5-yr Australian Dark Energy Survey reverberation mapping programme, which substantially improves the number and quality of Mg II lag measurements. As the Mg II feature is somewhat blended with iron emission, we model and subtract both the continuum and iron contamination from the multiepoch spectra before analysing the Mg II line. We also develop a new method of quantifying correlated spectroscopic calibration errors based on our numerous, contemporaneous observations of F-stars. The lag measurements for seven of our nine sources are consistent with both the H β and Mg II R−L relations reported by previous studies. Our simulations verify the lag reliability of our nine measurements, and we estimate that the median false positive rate of the lag measurements is 4 per cent⁠., We thank the anonymous referee for the careful review and comments. We thank Dr. Yue Shen for the constructive comments. ZY was supported in part by the United States National Science Foundation under Grant No. 161553 to PM. PM is grateful for support from the Radcliffe Institute for Advanced Study at Harvard University. PM also acknowledges support from the United States Department of Energy, Office of High Energy Physics under Award Number DE-SC-0011726. AP and UM are supported by the Australian Government Research Training Programme (RTP) Scholarship. This research was supported in part by the Australian Government through the Australian Research Council Laureate Fellowship funding scheme (project FL180100168). Based in part on data acquired at the Anglo-Australian Telescope, under programme A/2013B/012]. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaroi people, and pay our respects to elders past and present. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

DES Collaboration: et al., The DES-CMASS sample (DMASS) is designed to optimally combine the weak lensing measurements from the Dark Energy Survey (DES) and redshift-space distortions (RSD) probed by the CMASS galaxy sample from the Baryonic Oscillation Spectroscopic Survey. In this paper, we demonstrate the feasibility of adopting DMASS as the equivalent of CMASS for a joint analysis of DES and BOSS in the framework of modified gravity. We utilize the angular clustering of the DMASS galaxies, cosmic shear of the DES METACALIBRATION sources, and cross-correlation of the two as data vectors. By jointly fitting the combination of the data with the RSD measurements from the CMASS sample and Planck data, we obtain the constraints on modified gravity parameters μ0=−0.37+0.47−0.45 and Σ0=0.078+0.078−0.082⁠. Our constraints of modified gravity with DMASS are tighter than those with the DES Year 1 REDMAGIC sample with the same external data sets by 29 per cent for μ0 and 21 per cent for Σ0, and comparable to the published results of the DES Year 1 modified gravity analysis despite this work using fewer external data sets. This improvement is mainly because the galaxy bias parameter is shared and more tightly constrained by both CMASS and DMASS, effectively breaking the degeneracy between the galaxy bias and other cosmological parameters. Such an approach to optimally combine photometric and spectroscopic surveys using a photometric sample equivalent to a spectroscopic sample can be applied to combining future surveys having a limited overlap such as DESI and LSST., AC acknowledges support from NASA grant no. 15-WFIRST15-0008. During the preparation of this paper, C.H. was supported by the Simons Foundation, NASA, and the U.S. Department of Energy. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant no. 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science.

Jeffrey, N., et al. DES Collaboration, We present reconstructed convergence maps, mass maps, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (primarily dark matter) in the foreground of the observed galaxies. We use four reconstruction methods, each is a maximum a posteriori estimate with a different model for the prior probability of the map: Kaiser–Squires, null B-mode prior, Gaussian prior, and a sparsity prior. All methods are implemented on the celestial sphere to accommodate the large sky coverage of the DES Y3 data. We compare the methods using realistic ΛCDM simulations with mock data that are closely matched to the DES Y3 data. We quantify the performance of the methods at the map level and then apply the reconstruction methods to the DES Y3 data, performing tests for systematic error effects. The maps are compared with optical foreground cosmic-web structures and are used to evaluate the lensing signal from cosmic-void profiles. The recovered dark matter map covers the largest sky fraction of any galaxy weak lensing map to date., The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This paper has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. NJ has been supported by funding from l’Ecole Normale Supérieure, Paris. OL and NJ acknowledge support from a European Research Council Advanced Grant TESTDE (FP7/291329) and STFC Consolidated Grants ST/M001334/1 and ST/R000476/1. AK has been supported by a Juan de la Cierva fellowship from MINECO with project number IJC2018-037730-I, and funding for this project was also available in part through SEV-2015-0548 and AYA2017-89891-P. Cosmic voids computational work has been performed on the UK SCIAMA High Performance Computing cluster supported by the ICG, SEPNet, and the University of Portsmouth.

A. Leauthaud et al., Lensing without borders is a cross-survey collaboration created to assess the consistency of galaxy–galaxy lensing signals (ΔΣ) across different data sets and to carry out end-to-end tests of systematic errors. We perform a blind comparison of the amplitude of ΔΣ using lens samples from BOSS and six independent lensing surveys. We find good agreement between empirically estimated and reported systematic errors which agree to better than 2.3σ in four lens bins and three radial ranges. For lenses with zL > 0.43 and considering statistical errors, we detect a 3–4σ correlation between lensing amplitude and survey depth. This correlation could arise from the increasing impact at higher redshift of unrecognized galaxy blends on shear calibration and imperfections in photometric redshift calibration. At zL > 0.54, amplitudes may additionally correlate with foreground stellar density. The amplitude of these trends is within survey-defined systematic error budgets that are designed to include known shear and redshift calibration uncertainty. Using a fully empirical and conservative method, we do not find evidence for large unknown systematics. Systematic errors greater than 15 per cent (25 per cent) ruled out in three lens bins at 68 per cent (95 per cent) confidence at z < 0.54. Differences with respect to predictions based on clustering are observed to be at the 20–30 per cent level. Our results therefore suggest that lensing systematics alone are unlikely to fully explain the ‘lensing is low’ effect at z < 0.54. This analysis demonstrates the power of cross-survey comparisons and provides a promising path for identifying and reducing systematics in future lensing analyses., We acknowledge use of the lux supercomputer at UC Santa Cruz, funded by NSF MRI grant AST 1828315. This material is based on work supported by the U.D Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0019301. AL acknowledges support from the David and Lucille Packard foundation, and from the Alfred.P Sloan foundation. CH acknowledges support from the European Research Council under grant number 647112, and support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. KK acknowledges support from the Royal Society and Imperial College. HH is supported by a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1), as well as an ERC Consolidator Grant (no. 770935). AHW is supported by an European Research Council Consolidator Grant (no. 770935). Funding for the DES Projects has been provided by the U.S. Department of Energy; the U.S. National Science Foundation; the Ministry of Science and Education of Spain; the Science and Technology Facilities Council of the United Kingdom; the Higher Education Funding Council for England; the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign; the Kavli Institute of Cosmological Physics at the University of Chicago; the Center for Cosmology and Astro-Particle Physics at the Ohio State University; the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University; Financiadora de Estudos e Projetos; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência; Tecnologia e Inovação; the Deutsche Forschungsgemeinschaft; and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory; the University of California at Santa Cruz; the University of Cambridge; Centro de Investigaciones Energéticas; Medioambientales y Tecnológicas-Madrid; the University of Chicago; University College London; the DES-Brazil Consortium; the University of Edinburgh; the Eidgenössische Technische Hochschule (ETH) Zürich; Fermi National Accelerator Laboratory; the University of Illinois at Urbana-Champaign; the Institut de Ciències de l’Espai (IEEC/CSIC); the Institut de Física d’Altes Energies; Lawrence Berkeley National Laboratory; the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe; the University of Michigan; NFS’s NOIRLab; the University of Nottingham; The Ohio State University; the University of Pennsylvania; the University of Portsmouth; SLAC National Accelerator Laboratory; Stanford University; the University of Sussex; Texas A&M University; and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The Hyper Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan and Taiwan, and Princeton University. The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High Energy Accelerator Research Organisation (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from the Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University. This paper makes use of software developed for the Large Synoptic Survey Telescope. We thank the LSST Project for making their code available as free software at http://dm.lsst.org This paper is based [in part] on data collected at the Subaru Telescope and retrieved from the HSC data archive system, which is operated by Subaru Telescope and Astronomy Data Center (ADC) at NAOJ. Data analysis was in part carried out with the cooperation of Center for Computational Astrophysics (CfCA), NAOJ. The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg, and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen’s University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation grant no. AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation. Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme IDs 177.A-3016, 177.A-3017, 177.A-3018, and 179.A-2004, and on data products produced by the KiDS consortium. The KiDS production team acknowledges support from: Deutsche Forschungsgemeinschaft, ERC, NOVA, and NWO-M grants; Target; the University of Padova, and the University Federico II (Naples). This work was supported by the Department of Energy, Laboratory Directed Research and Development program at SLAC National Accelerator Laboratory, under contract DE-AC02-76SF00515 and as a part of the Panofsky Fellowship awarded to DG. MM is partially funded by FAPERJ, CNPq, and CONICET. BM acknowledges support from the Brazilian funding agency FAPERJ.

The AAA+ protein disaggregase, Hsp104, increases fitness under stress by reversing stress-induced protein aggregation. Natural Hsp104 variants might exist with enhanced, selective activity against neurodegenerative disease substrates. However, natural Hsp104 variation remains largely unexplored. Here, we screened a cross-kingdom collection of Hsp104 homologs in yeast proteotoxicity models. Prokaryotic ClpG reduced TDP-43, FUS, and α-synuclein toxicity, whereas prokaryotic ClpB and hyperactive variants were ineffective. We uncovered therapeutic genetic variation among eukaryotic Hsp104 homologs that specifically antagonized TDP-43 condensation and toxicity in yeast and TDP-43 aggregation in human cells. We also uncovered distinct eukaryotic Hsp104 homologs that selectively antagonized α-synuclein condensation and toxicity in yeast and dopaminergic neurodegeneration inC. elegans. Surprisingly, this therapeutic variation did not manifest as enhanced disaggregase activity, but rather as increased passive inhibition of aggregation of specific substrates. By exploring natural tuning of this passive Hsp104 activity, we elucidated enhanced, substrate-specific agents that counter proteotoxicity underlying neurodegeneration.

DES Collaboration: et al., We present an analysis of DES17X1boj and DES16E2bjy, two peculiar transients discovered by the Dark Energy Survey (DES). They exhibit nearly identical double-peaked light curves that reach very different maximum luminosities (Mr = −15.4 and −17.9, respectively). The light-curve evolution of these events is highly atypical and has not been reported before. The transients are found in different host environments: DES17X1boj was found near the nucleus of a spiral galaxy, while DES16E2bjy is located in the outskirts of a passive red galaxy. Early photometric data are well fitted with a blackbody and the resulting moderate photospheric expansion velocities (1800 km s−1 for DES17X1boj and 4800 km s−1 for DES16E2bjy) suggest an explosive or eruptive origin. Additionally, a feature identified as high-velocity Ca II absorption (⁠v ≈ 9400 km s−1) in the near-peak spectrum of DES17X1boj may imply that it is a supernova. While similar light-curve evolution suggests a similar physical origin for these two transients, we are not able to identify or characterize the progenitors., We acknowledge support from the European Union’s 7th Framework Programme (EU/FP7) European Research council (ERC) grant 615929, and Science and Technology Facilities Council (STFC) grant ST/R000506/1. LG was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 839090. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).

We report the results of the STRong lensing Insights from the Dark Energy Survey (STRIDES) follow-up campaign of the late 2017/early 2018 season. We obtained spectra of 65 lensed quasar candidates either with EFOSC2 on the NTT or ESI on Keck, which confirm 10 new gravitationally lensed quasars and 10 quasar pairs with similar spectra, but which do not show a lensing galaxy in DES images. Eight lensed quasars are doubly imaged with source redshifts between 0.99 and 2.90, one is triply imaged by a group (DESJ0345-2545, $z=1.68$), and one is quadruply imaged (quad: DESJ0053-2012, $z=3.8$). Singular isothermal ellipsoid models for the doubles, based on high-resolution imaging from SAMI on SOAR or NIRC2 on Keck, give total magnifications between 3.2 and 5.6, and Einstein radii between 0.49 and 1.97 arcseconds. After spectroscopic follow-up, we extract multi-epoch $grizY$ photometry of confirmed lensed quasars and contaminant quasar+star pairs from the first 4 years of DES data using parametric multi-band modelling, and compare variability in each system's components. By measuring the reduced ${\chi}^2$ associated with fitting all epochs to the same magnitude, we find a simple cut on the less variable component that retains all confirmed lensed quasars, while removing 94 per cent of contaminant systems with stellar components. Based on our spectroscopic follow-up, this variability information can improve selection of lensed quasars and quasar pairs from 34-45 per cent to 51-70 per cent, with the majority of remaining contaminants being compact star-forming galaxies. Using mock lensed quasar lightcurves we demonstrate that selection based only on variability will over-represent the quad fraction by 10 per cent over a complete DES magnitude-limited sample (excluding microlensing differences), explained by the magnification bias and hence lower luminosity (more variable) sources in quads., Comment: 22 pages, 16 figures, submitted to MNRAS