Variable expressivity of a novel mutation in the SCN1A gene leading to an autosomal dominant seizure disorder

AbstractMutations in the SCN1A gene can cause a variety of dominantly inherited epilepsy syndromes. Severe phenotypes usually result from loss of function mutations, whereas missense mutations cause a milder phenotype by altering the sodium channel activity. We report on a novel missense variant (p.Val1379Leu) in the SCN1A gene segregating in an autosomal dominant pattern in a family exhibiting a variable epilepsy phenotype ranging from generalized epilepsy with febrile seizures during infancy to a well controlled seizure disorder in adulthood. This report supports the importance of SCN1A mutation analysis in families in which seizure disorders segregate in an autosomal dominant fashion

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio

Molecular and Historical Aspects of Corn Belt Dent Diversity

Tens-of-thousands of open-pollinated cultivars of corn (Zea mays L.) are being maintained in germplasm banks. Knowledge of the amount and distribution of genetic variation within and among accessions can aid end users in choosing among them. We estimated molecular genetic variation and looked for influences of pedigree, adaptation, and migration in the genetic makeup of conserved Corn-Belt Dent-related germplasm. Plants sampled from 57 accessions representing Corn-Belt Dents, Northern Flints, Southern Dents, plus 12 public inbreds, were genotyped at 20 simple sequence repeat (SSR) loci. For 47 of the accessions, between 5 and 23 plants per accession were genotyped (mean = 9.3). Mean number of alleles per locus was 6.5 overall, 3.17 within accessions, and 3.20 within pooled inbreds. Mean gene diversity was 0.53 within accessions and 0.61 within pooled inbreds. Open-pollinated accessions showed a tendency toward inbreeding (FIS = 0.09), and 85% of genetic variation was shared among them. A Fitch-Margoliash tree strongly supported the distinctiveness of flint from dent germplasm but did not otherwise reveal evidence of genetic structure. Mantel tests revealed significant correlations between genetic distance and geographical (r = 0.54, P= 0.04) or maturity zone (r = 0.33, P = 0.03) distance only if flint germplasm was included in the analyses. A significant correlation (r = 0.76, P \u3c 0.01) was found between days to pollen shed and maturity zone of accession origin. Pedigree, rather than migration or selection, has most influenced the genetic structure of the extant representatives of the open-pollinated cultivars at these SSR loci

Superluminous supernovae from the Dark Energy Survey

We present a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I) and one hydrogen-rich SLSN (SLSN-II) detected during the five-year Dark Energy Survey (DES). These SNe, located in the redshift range 0.220 < z < 1.998, represent the largest homogeneously selected sample of SLSN events at high redshift. We present the observed g, r, i, z light curves for these SNe, which we interpolate using Gaussian processes. The resulting light curves are analysed to determine the luminosity function of SLSNe-I, and their evolutionary timescales. The DES SLSN-I sample significantly broadens the distribution of SLSN-I light-curve properties when combined with existing samples from the literature. We fit a magnetar model to our SLSNe, and find that this model alone is unable to replicate the behaviour of many of the bolometric light curves. We search the DES SLSN-I light curves for the presence of initial peaks prior to the main light-curve peak. Using a shock breakout model, our Monte Carlo search finds that 3 of our 14 events with pre-max data display such initial peaks. However, 10 events show no evidence for such peaks, in some cases down to an absolute magnitude of<−16, suggesting that such features are not ubiquitous to all SLSN-I events. We also identify a red pre-peak feature within the light curve of one SLSN, which is comparable to that observed within SN2018bsz

Dark Energy Survey Year 1 Results: A Precise H0 Measurement from DES Y1, BAO, and D/H Data

We combine Dark Energy Survey Year 1 clustering and weak lensing data with baryon acoustic oscillations and Big Bang nucleosynthesis experiments to constrain the Hubble constant. Assuming a flat ΛCDM model with minimal neutrino mass (Σm υ = 0.06 eV), we find H 0 = 67.4 -1.2+1.1 km s -1 Mpc -1 (68 per cent CL). This result is completely independent of Hubble constant measurements based on the distance ladder, cosmic microwave background anisotropies (both temperature and polarization), and strong lensing constraints. There are now five data sets that: (a) have no shared observational systematics; and (b) each constrains the Hubble constant with fractional uncertainty at the few percent level. We compare these five independent estimates, and find that, as a set, the differences between them are significant at the 2.5σ level (χ 2 /dof = 24/11, probability to exceed = 1.1 per cent). Having set the threshold for consistency at 3σ, we combine all five data sets to arrive at H 0 = 69.3 -0.6+0.4 km s -1 Mpc -

The VANDELS ESO public spectroscopic survey

VANDELS is a uniquely deep spectroscopic survey of high-redshift galaxies with the VIMOS spectrograph on ESO’s Very Large Telescope (VLT). The survey has obtained ultradeep optical (0.48 < λ < 1.0 μ m) spectroscopy of ≃2100 galaxies within the redshift interval 1.0 ≤ z ≤ 7.0, over a total area of ≃0.2 deg2 centred on the CANDELS Ultra Deep Survey and Chandra Deep Field South fields. Based on accurate photometric redshift pre-selection, 85 per cent of the galaxies targeted by VANDELS were selected to be at z ≥ 3. Exploiting the red sensitivity of the refurbished VIMOS spectrograph, the fundamental aim of the survey is to provide the high-signal-to-noise ratio spectra necessary to measure key physical properties such as stellar population ages, masses, metallicities, and outflow velocities from detailed absorption-line studies. Using integration times calculated to produce an approximately constant signal-to-noise ratio (20 < tint< 80 h), the VANDELS survey targeted: (a) bright star-forming galaxies at 2.4 ≤ z ≤ 5.5, (b) massive quiescent galaxies at 1.0 ≤ z ≤ 2.5, (c) fainter star-forming galaxies at 3.0 ≤ z ≤ 7.0, and (d) X-ray/Spitzer-selected active galactic nuclei and Herschel-detected galaxies. By targeting two extragalactic survey fields with superb multiwavelength imaging data, VANDELS will produce a unique legacy data set for exploring the physics underpinning high-redshift galaxy evolution. In this paper, we provide an overview of the VANDELS survey designed to support the science exploitation of the first ESO public data release, focusing on the scientific motivation, survey design, and target selection

DES science portal: Computing photometric redshifts

A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-z methods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-z training, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-based data interface that not only provides the framework to carry out the above steps in a systematic way, enabling the ease testing and comparison of different algorithms, but also addresses the processing requirements by parallelizing the calculation in a transparent way for the user. This framework called the Science Portal (hereafter Portal) was developed in the context the Dark Energy Survey (DES) to facilitate scientific analysis. In this paper, we show how the Portal can provide a reliable environment to access vast datasets, provide validation algorithms and metrics, even in the case of multiple photo-zs methods. It is possible to maintain the provenance between the steps of a chain of workflows while ensuring reproducibility of the results. We illustrate how the Portal can be used to provide photo-z estimates using the DES first year (Y1A1) data. While the DES collaboration is still developing techniques to obtain more precise photo-zs, having a structured framework like the one presented here is critical for the systematic vetting of DES algorithmic improvements and the consistent production of photo-zs in future DES releases

Measurement of the splashback feature around SZ-selected Galaxy clusters with DES, SPT, and ACT

We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3 data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected redMaPPer clusters with similar mass and redshift distributions, rsp is ∼2σ smaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first infall into the cluster

First cosmology results using SNe Ia from the dark energy survey: analysis, systematic uncertainties, and validation

International audienceWe present the analysis underpinning the measurement of cosmological parameters from 207 spectroscopically classified type Ia supernovae (SNe Ia) from the first three years of the Dark Energy Survey Supernova Program (DES-SN), spanning a redshift range of 0.01

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)