Internal and external variability in regional simulations of the Iberian Peninsula climate over the last millennium

In this study we analyse the role of internal variability in regional climate simulations through a comparison of two regional paleoclimate simulations for the last millennium. They share the same external forcings and model configuration, differing only in the initial condition used to run the driving global model simulation. A comparison of these simulations allows us to study the role of internal variability in climate models at regional scales, and how it affects the long-term evolution of climate variables such as temperature and precipitation. The results indicate that, although temperature is homogeneously sensitive to the effect of external forcings, the evolution of precipitation is more strongly governed by random unpredictable internal dynamics. There are, however, some areas where the role of internal variability is lower than expected, allowing precipitation to respond to the external forcings. In this respect, we explore the underlying physical mechanisms responsible for it. This study identifies areas, depending on the season, in which a direct comparison between model simulations of precipitation and climate reconstructions would be meaningful, but also other areas where good agreement between them should not be expected even if both are perfect

DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey data sets, we designed ZipperNet, a multibranch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories—no lens, galaxy-galaxy lens, lensed Type-Ia supernova, lensed core-collapse supernova—within high-fidelity simulations of three cosmic survey data sets: the Dark Energy Survey, Rubin Observatory’s Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like data set, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1–2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments

DES15E2mlf: a spectroscopically confirmed superluminous supernova that exploded 3.5 Gyr after the big bang

We present the Dark Energy Survey (DES) discovery of DES15E2mlf, the most distant superluminous supernova (SLSN) spectroscopically confirmed to date. The light curves and Gemini spectroscopy of DES15E2mlf indicate that it is a Type I superluminous supernova (SLSN-I) at z = 1.861 (a lookback time of ∼10 Gyr) and peaking at MAB = −22.3 ± 0.1 mag. Given the high redshift, our data probe the rest-frame ultraviolet (1400–3500 Å) properties of the SN, finding velocity of the C III feature changes by ∼5600 km s−1 over 14 d around maximum light. We find the host galaxy of DES15E2mlf has a stellar mass of 3.5+3.6 −2.4 × 109 M, which is more massive than the typical SLSN-I host galaxy

DES14X3taz: a type I superluminous supernova showing a luminous, rapidly cooling initial pre-peak bump

We present DES14X3taz, a new hydrogen-poor superluminous supernova (SLSN-I) discovered by the Dark Energy Survey (DES) supernova program, with additional photometric data provided by the Survey Using DECam for Superluminous Supernovae. Spectra obtained using Optical System for Imaging and low-Intermediate-Resolution Integrated Spectroscopy on the Gran Telescopio CANARIAS show DES14X3taz is an SLSN-I at z = 0.608. Multi-color photometry reveals a double-peaked light curve: a blue and relatively bright initial peak that fades rapidly prior to the slower rise of the main light curve. Our multi-color photometry allows us, for the first time, to show that the initial peak cools from 22,000 to 8000 K over 15 rest-frame days, and is faster and brighter than any published core-collapse supernova, reaching 30% of the bolometric luminosity of the main peak. No physical 56Ni-powered model can fit this initial peak. We show that a shock-cooling model followed by a magnetar driving the second phase of the light curve can adequately explain the entire light curve of DES14X3taz. Models involving the shock-cooling of extended circumstellar material at a distance of ~=400 {\text{}}{R}&sun; are preferred over the cooling of shock-heated surface layers of a stellar envelope. We compare DES14X3taz to the few double-peaked SLSN-I events in the literature. Although the rise times and characteristics of these initial peaks differ, there exists the tantalizing possibility that they can be explained by one physical interpretation

Studying the ultraviolet spectrum of the first spectroscopically confirmed supernova at redshift two

We present observations of DES16C2nm, the first spectroscopically confirmed hydrogen-free superluminous supernova (SLSN-I) at redshift z » 2. DES16C2nm was discovered by the Dark Energy Survey (DES) Supernova Program, with follow-up photometric data from the Hubble Space Telescope, Gemini, and the European Southern Observatory Very Large Telescope supplementing the DES data. Spectroscopic observations confirm DES16C2nm to be at z = 1.998, and spectroscopically similar to Gaia16apd (a SLSN-I at z = 0.102), with a peak absolute magnitude of U =- 22.26 0.06. The high redshift of DES16C2nm provides a unique opportunity to study the ultraviolet (UV) properties of SLSNe-I. Combining DES16C2nm with 10 similar events from the literature, we show that there exists a homogeneous class of SLSNe-I in the UV (lrest » 2500 Å), with peak luminosities in the (rest-frame) U band, and increasing absorption to shorter wavelengths. There is no evidence that the mean photometric and spectroscopic properties of SLSNe-I differ between low (z 1), but there is clear evidence of diversity in the spectrum at lrest 2 these events appear optically red, peaking in the observer-frame z-band. Such characteristics are critical to identify these objects with future facilities such as the Large Synoptic Survey Telescope, Euclid, and the Wide-field Infrared Survey Telescope, which should detect such SLSNe-I to z = 3.5, 3.7, and 6.6, respectively

SILEX: a fast and inexpensive high-quality DNA extraction method suitable for multiple sequencing platforms and recalcitrant plant species

[EN] Background The use of sequencing and genotyping platforms has undergone dramatic improvements, enabling the generation of a wealth of genomic information. Despite this progress, the availability of high-quality genomic DNA (gDNA) in sufficient concentrations is often a main limitation, especially for third-generation sequencing platforms. A variety of DNA extraction methods and commercial kits are available. However, many of these are costly and frequently give either low yield or low-quality DNA, inappropriate for next generation sequencing (NGS) platforms. Here, we describe a fast and inexpensive DNA extraction method (SILEX) applicable to a wide range of plant species and tissues. Results SILEX is a high-throughput DNA extraction protocol, based on the standard CTAB method with a DNA silica matrix recovery, which allows obtaining NGS-quality high molecular weight genomic plant DNA free of inhibitory compounds. SILEX was compared with a standard CTAB extraction protocol and a common commercial extraction kit in a variety of species, including recalcitrant ones, from different families. In comparison with the other methods, SILEX yielded DNA in higher concentrations and of higher quality. Manual extraction of 48 samples can be done in 96 min by one person at a cost of 0.12 euro/sample of reagents and consumables. Hundreds of tomato gDNA samples obtained with either SILEX or the commercial kit were successfully genotyped with Single Primer Enrichment Technology (SPET) with the Illumina HiSeq 2500 platform. Furthermore, DNA extracted fromSolanum elaeagnifoliumusing this protocol was assessed by Pulsed-field gel electrophoresis (PFGE), obtaining a suitable size ranges for most sequencing platforms that required high-molecular-weight DNA such as Nanopore or PacBio. Conclusions A high-throughput, fast and inexpensive DNA extraction protocol was developed and validated for a wide variety of plants and tissues. SILEX offers an easy, scalable, efficient and inexpensive way to extract DNA for various next-generation sequencing applications including SPET and Nanopore among others.This research has been funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 677379 (Linking genetic resources, genomes and phenotypes of Solanaceous crops; G2P-SOL). David Alonso is grateful to Universitat Politecnica de Valencia for a predoctoral (PAID-01-16) contract under the Programa de Ayudas de Investigacion y Desarrollo initiative. Mariola Plazas is grateful to Generalitat Valenciana and Fondo Social Europeo for a postdoctoral grant (APOSTD/2018/014). Pietro Gramazio is grateful to Japan Society for the Promotion of Science for a Postdoctoral Grant (P19105, FY2019 JSPS Postdoctoral Fellowship for Research in Japan (Standard)). The Spanish Ministerio de Educacion, Cultura y Deporte funded a predoctoral fellowship granted to Edgar Garcia-Fortea (FPU17/02389).Vilanova Navarro, S.; Alonso-Martín, D.; Gramazio, P.; Plazas Ávila, MDLO.; García-Fortea, E.; Ferrante, P.; Schmidt, M.... (2020). SILEX: a fast and inexpensive high-quality DNA extraction method suitable for multiple sequencing platforms and recalcitrant plant species. Plant Methods. 16(1):1-11. https://doi.org/10.1186/s13007-020-00652-yS111161Scheben A, Batley J, Edwards D. Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J. 2017;15:149–61.Jung H, Winefield C, Bombarely A, Prentis P, Waterhouse P. Tools and strategies for long-read sequencing and de novo assembly of plant genomes. Trends Plant Sci. 2019;24:700–24.Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE. 2011;6:e19379.Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE. 2008;3:e3376.Scaglione D, Pinosio S, Marroni F, Centa E, Fornasiero A, Magris G, Scalabrin S, Cattonaro F, Taylor G, Morgante M. Single primer enrichment technology as a tool for massive genotyping: a benchmark on black poplar and maize. Ann Bot. 2019;124:543–51.Barchi L, Acquadro A, Alonso D, Aprea G, Bassolino L, Demurtas O, Ferrante P, Gramazio P, Mini P, Portis E, Scaglione D, Toppino L, Vilanova S, Díez MJ, Rotino G, Lanteri S, Prohens J, Giuliano G. Single primer enrichment technology (SPET) for high-throughput genotyping in tomato and eggplant germplasm. Front Plant Sci. 2019;10:1005.Vaillancourt B, Buell CR. High molecular weight DNA isolation method from diverse plant species for use with Oxford Nanopore sequencing. bioRxiv. 2019;1:783159.Anderson CB, Franzmayr BK, Hong SW, Larking AC, van Stijn TC, Tan R, Moraga R, Faville M, Griffiths A. Protocol: a versatile, inexpensive, high-throughput plant genomic DNA extraction method suitable for genotyping-by-sequencing. Plant Methods. 2018;14:75.Rana MM, Aycan M, Takamatsu T, Kaneko K, Mitsui T, Itoh K. Optimized nuclear pellet method for extracting next-generation sequencing quality genomic DNA from fresh leaf tissue. Methods Protoc. 2019;2:54.Doyle JJ, Doyle JL. Isolation of plant DNA from fresh tissue. Focus. 1990;12:13–5.Healey A, Furtado A, Cooper T, Henry RJ. Protocol: a simple method for extracting next-generation sequencing quality genomic DNA from recalcitrant plant species. Plant Methods. 2014;10:21.Martínez-González CR, Ramírez-Mendoza R, Jiménez-Ramírez J, Gallegos-Vázquez C, Luna-Vega I. Improved method for genomic DNA extraction for Opuntia Mill. (Cactaceae). Plant Methods. 2017;13:82.Barbier FF, Chabikwa TG, Ahsan MU, Cook SE, Powell R, Tanurdzic M, Beveridge C. A phenol/chloroform-free method to extract nucleic acids from recalcitrant, woody tropical species for gene expression and sequencing. Plant Methods. 2019;15:62.Souza DC, Teixeira TA. A simple and effective method to obtain high DNA quality and quantity from Cerrado plant species. Mol Biol Rep. 2019;46:4611–5.Kovačević N. Magnetic beads based nucleic acid purification for molecular biology applications. Sample preparation techniques for soil, plant, and animal samples. In: Micic M, editor. Springer Protoc Handb. 2016;53–67.Martin SL, Parent JS, Laforest M, Page E, Kreiner JM, James T. Population genomic approaches for weed science. Plants. 2019;8:354.Zhou Y, Zhang Y, He W, Wang J, Peng F, Huang L, Zhao S, Deng W. Rapid regeneration and reuse of silica columns from PCR purification and gel extraction kits. Sci Rep. 2018;8:12870.Park HJ, Cho H, Jung HS, Cho BH, Lee MY. Development of a DNA isolation device using poly(3,4-dihydroxy-l-phenylalanine)-coated swab for on-site molecular diagnostics. Sci Rep. 2019;9:8144.Boom R, Sol CJ, Salimans MM, Jansen CL, Wertheim-van Dillen PM, van der Noordaa J. Rapid and simple method for purification of nucleic acids. J Clin Microbiol. 1990;28:495–503.Carter MJ, Milton ID. An inexpensive and simple method for DNA purifications on silica particles. Nucleic Acids Res. 1993;21:1044.Carvalho J, Puertas G, Gaspar J, Azinheiro S, Diéguez L, Garrido-Maestu A, Vázquez M, Barros-Velázquez J, Cardoso S, Padro M. Highly efficient DNA extraction and purification from olive oil on a washable and reusable miniaturized device. Anal Chim Acta. 2018;1020:30–40.Branton D, Deamer D, Quick J, Loman NJ. DNA extraction strategies for nanopore sequencing. Nanopore Seq. World Sci. 2019;1:91–105.Cheng H, Zhang K, Libera J, De La Cruz M, Bedzyk M. Polynucleotide adsorption to negatively charged surfaces in divalent salt solutions. Biophys J. 2016;90:1164–74.Shi B, Shin Y, Hassanali A, Singer S. DNA Binding to the Silica Surface. J Phys Chem B. 2015;119:11030–40.Katevatis C, Fan A, Klapperich CM. Low concentration DNA extraction and recovery using a silica solid phase. PLoS ONE. 2017;12:e0176848.Green MR, Sambrook J. Isolation and quantification of DNA. Cold Spring Harb Protoc. 2018;2018:403–14.Toole K, Roffey P, Young E, Cho K, Shaw T, Smith M, Blagojevic N. Evaluation of commercial forensic DNA extraction kits for decontamination and extraction of DNA from biological samples contaminated with radionuclides. Forensic Sci Int. 2019;302:109867.Piskata Z, Servusova E, Babak V, Nesvadbova M, Borilova G. The quality of DNA isolated from processed food and feed via different extraction procedures. Molecules. 2019;24:1188.Xia Y, Chen F, Du Y, Liu C, Bu G, Xin Y, Boye L. A modified SDS-based DNA extraction method from raw soybean. Biosci Rep. 2019;39:2.Akkurt M. Comparison between modified DNA extraction protocols and commercial isolation kits in grapevine (Vitis vinifera L.). Genet Mol Res. 2012;11:2343–51.Marsal G, Baiges I, Canals JM, Zamora F, Fort F. A Fast, efficient method for extracting DNA from leaves, stems, and seeds of Vitis vinifera L. Am J Enol Vitic. 2011;62:376–81.Abdel-Latif A, Osman G. Comparison of three genomic DNA extraction methods to obtain high DNA quality from maize. Plant Methods. 2017;13:1.Huang J, Ge X, Sun M. Modified CTAB protocol using a silica matrix for isolation of plant genomic DNA. Biotechniques. 2000;28:432–4.Rogstad SH. Plant DNA extraction using silica. Plant Mol Biol Report. 2012;21:463.Li J-F, Li L, Sheen J. Protocol: a rapid and economical procedure for purification of plasmid or plant DNA with diverse applications in plant biology. Plant Methods. 2010;6:1.Li J-F, Sheen J. DNA purification from multiple sources in plant research with homemade silica resins. Humana Press. 2012;862:53–9.Vandeventer PE, Lin JS, Zwang TJ, Nadim A, Johal MS, Niemz A. Multiphasic DNA adsorption to silica surfaces under varying buffer, pH, and ionic strength conditions. J Phys Chem B. 2012;116:5661–70.Boesenberg-Smith KA, Pessarakli MM, Wolk DM. Assessment of DNA yield and purity: an overlooked detail of PCR troubleshooting. Clin Microbiol Newsl. 2012;34:1–6.Emaus MN, Clark KD, Hinners P, Anderson JL. Preconcentration of DNA using magnetic ionic liquids that are compatible with real-time PCR for rapid nucleic acid quantification. Anal Bioanal Chem. 2018;410:4135–44.Dumschott K, Schmidt MHW, Chawla HS, Snowdon R, Usadel B. Oxford Nanopore sequencing: new opportunities for plant genomics? J Exp Bot. 2020;eraa263Knapp S, Sagona E, Carbonell AKZ, Chiarini F. A revision of the Solanum elaeagnifolium clade (Elaeagnifolium clade; subgenus Leptostemonum, Solanaceae). PhytoKeys. 2017;84:1–104.García-Fortea E, Gramazio P, Vilanova S, Fita A, Mangino G, Villanueva G, Arrones A, Knapp S, Prohens J, Plazas M. First successful backcrossing towards eggplant (Solanum melongena) of a New World species, the silverleaf nightshade (S. elaeagnifolium), and characterization of interspecific hybrids and backcrosses. Sci Hort. 2019;246:563–73.Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat. 1996;5:3299–314.Wickham H. ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag; 2016.Ponti G, Maccaferri M, Manfredini M, Kaleci S, Mandrioli M, Pellacani G, Ozben T, Depenni R, Bianchi G, Pirola G, Tomasi A. The value of fluorimetry (Qubit) and spectrophotometry (NanoDrop) in the quantification of cell-free DNA (cfDNA) in malignant melanoma and prostate cancer patients. Clin Chim Acta. 2018;479:14–9.Lakshmi R, Baskar V, Ranga U. Extraction of superior-quality plasmid DNA by a combination of modified alkaline lysis and silica matrix. Anal Biochem. 1999;272:109–12.Taylor JI, Hurst CD, Davies MJ, Sachsinger N, Bruce IJ. Application of magnetite and silica–magnetite composites to the isolation of genomic DNA. J Chromatogr A. 2000;890:159–66.Prodělalová J, Rittich B, Španová A, Petrová K, Beneš MJ. Isolation of genomic DNA using magnetic cobalt ferrite and silica particles. J Chromatogr A. 2004;1056:43–8.Shan Z, Jiang Y, Guo M, Bennett JC, Li X, Tian H, Oakes K, Zhang, Zhou Y, Huang Q, Chen H. Promoting DNA loading on magnetic nanoparticles using a DNA condensation strategy. Colloids Surfaces B Biointerfaces. 2015;125:247–54.Greco M, Sáez C, Brown M, Bitonti M. A simple and effective method for high quality co-extraction of genomic DNA and total RNA from low biomass Ectocarpus siliculosus, the model brown alga. PLoS ONE. 2014;9:e96470.Schrader C, Schielke A, Ellerbroek L, Johne R. PCR inhibitor – occurrence, properties and removal. J Appl Microbiol. 2012;113:1014–26.Demeke T, Adams RP. The effects of plant polysaccharides and buffer additives on PCR. Biotechniques. 1992;12:332–4.Asami DK, Hong YJ, Barrett DM, Mitchell AE. Comparison of the total phenolic and ascorbic acid content of freeze-dried and air-dried marionberry, strawberry, and corn grown using conventional, organic, and sustainable agricultural practices. J Agric Food Chem. 2003;51:1237–41.Schmidt M, Vogel A, Denton A, Istace B, Wormit A, van de Geest H, Bolger M, Alseekh S, Maß J, Pfaff C, Schurr U, Chetelat R, Maumus F, Aury J, Koren S, Fernie A, Zamir D, Bolger A, Usadel B. De novo assembly of a new Solanum pennellii accession using nanopore sequencing. Plant cell. 2017;29:2336–48

First cosmological results using Type Ia supernovae from the Dark Energy Survey: measurement of the Hubble constant

We present an improved measurement of the Hubble constant (H0) using the `inverse distance ladder' method, which adds the information from 207 Type Ia supernovae (SNe Ia) from the Dark Energy Survey (DES) at redshift 0.018 < z < 0.85 to existing distance measurements of 122 low-redshift (z < 0.07) SNe Ia (Low-z) and measurements of Baryon Acoustic Oscillations (BAOs). Whereas traditional measurements of H0 with SNe Ia use a distance ladder of parallax and Cepheid variable stars, the inverse distance ladder relies on absolute distance measurements from the BAOs to calibrate the intrinsic magnitude of the SNe Ia. We find H0 = 67.8 ± 1.3 km s-1 Mpc-1 (statistical and systematic uncertainties, 68 per cent confidence). Our measurement makes minimal assumptions about the underlying cosmological model, and our analysis was blinded to reduce confirmation bias. We examine possible systematic uncertainties and all are below the statistical uncertainties. Our H0 value is consistent with estimates derived from the Cosmic Microwave Background assuming a ΛCDM universe

First cosmology results using type Ia supernovae from the Dark Energy Survey: constraints on cosmological parameters

We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat LCDM model we find a matter density Omega_m = 0.331 +_ 0.038. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 +_ 0.059, and Omega_m = 0.321 +_ 0.018. For a flat w0waCDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w0 = -0.885 +_ 0.114 and wa = -0.387 +_ 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA)