Deep Extragalactic VIsible Legacy Survey: Data Release 1 blended spectra search for candidate strong gravitational lenses

Here, we present a catalogue of blended spectra in Data Release 1 of the Deep Extragalactic VIsible Legacy Survey (DEVILS) on the Anglo-Australian Telescope. Of the 23 197 spectra, 181 showed signs of a blend of redshifts and spectral templates. We examine these blends in detail for signs of either a candidate strong lensing galaxy or a useful overlapping galaxy pair. One of the three DEVILS target fields, COSMOS (D10), is close to complete and it is fully imaged with Hubble Space Telescope Advanced Camera for Surveys, and we visually examine the 57 blended spectra in this field in the F814W postage stamps. Nine are classical strong lensing candidates with an elliptical as the lens, out to higher redshifts than any previous search with spectroscopic surveys such as Sloan Digital Sky Survey (SDSS) or Galaxy And Mass Assembly. The gravitational lens candidate success rate is similar to earlier such searches (0.1 per cent). Strong gravitational lenses identified with blended spectroscopy have typically shown a high success rate (\u3e70 per cent), which make these interesting targets for future higher resolution lensing studies, monitoring for supernova cosmography, or searches for magnified atomic hydrogen signal

Deep Extragalactic VIsible Legacy Survey: Data Release 1 blended spectra search for candidate strong gravitational lenses

Here, we present a catalogue of blended spectra in Data Release 1 of the Deep Extragalactic VIsible Legacy Survey (DEVILS) on the Anglo-Australian Telescope. Of the 23 197 spectra, 181 showed signs of a blend of redshifts and spectral templates. We examine these blends in detail for signs of either a candidate strong lensing galaxy or a useful overlapping galaxy pair. One of the three DEVILS target fields, COSMOS (D10), is close to complete and it is fully imaged with Hubble Space Telescope Advanced Camera for Surveys, and we visually examine the 57 blended spectra in this field in the F814W postage stamps. Nine are classical strong lensing candidates with an elliptical as the lens, out to higher redshifts than any previous search with spectroscopic surveys such as Sloan Digital Sky Survey (SDSS) or Galaxy And Mass Assembly. The gravitational lens candidate success rate is similar to earlier such searches (0.1 per cent). Strong gravitational lenses identified with blended spectroscopy have typically shown a high success rate (\u3e70 per cent), which make these interesting targets for future higher resolution lensing studies, monitoring for supernova cosmography, or searches for magnified atomic hydrogen signal

Identification of a major Listeria monocytogenes outbreak clone linked to soft cheese in Northern Italy - 2009-2011

Background: Molecular subtyping and enhanced surveillance in Lombardy region identified a cluster of possibly related listeriosis cases from 2006 to 2010. This cluster grouped 31 isolates that belonged to serotype 1/2a and Sequence Type 38 (ST38) as defined by Multilocus Sequence Typing (MLST). Methods: Our study expanded the previous investigation to include cases from 2011 to 2014 and used Multi-Virulence- Locus Sequence Typing (MVLST) on all ST38 isolates to better understand their epidemiology and possibly identify a common source outbreak. Results: Out of 306 L. monocytogenes clinical isolates collected, 43 (14.1%) belonged to ST38 with cases occurring in nine out of twelve Lombardy provinces. The ST38 isolates were split by MVLST into two Virulence Types (VTs): VT80 (n = 12) and VT104 (n = 31). VT104 cases were concentrated between 2009 and 2011 in two provinces, Bergamo and Milan. An epidemiologic investigation was performed and in one case, a matching VT104 isolate was retrieved from a soft cheese sample from a patient's refrigerator. Conclusions: Our findings revealed a major listeriosis outbreak in Northern Italy linked to soft cheese in 20092011, which went undetected by local health authorities. Our study shows that integrating subtyping methods with conventional epidemiology can help identify the source of L. monocytogenes outbreak clones

Modelling strong lenses from wide-field ground-based observations in KiDS and GAMA

Despite the success of galaxy-scale strong gravitational lens studies with Hubble-quality imaging, a number of well-studied strong lenses remains small. As a result, robust comparisons of the lens models to theoretical predictions are difficult. This motivates our application of automated Bayesian lens modelling methods to observations from public data releases of overlapping large ground-based imaging and spectroscopic surveys: Kilo-Degree Survey (KiDS) and Galaxy and Mass Assembly (GAMA), respectively. We use the open-source lens modelling software PYAUTOLENS to perform our analysis. We demonstrate the feasibility of strong lens modelling with large-survey data at lower resolution as a complementary avenue to studies that utilize more time-consuming and expensive observations of individual lenses at higher resolution. We discuss advantages and challenges, with special consideration given to determining background source redshifts from single-aperture spectra and to disentangling foreground lens and background source light. High uncertainties in the best-fitting parameters for the models due to the limits of optical resolution in ground-based observatories and the small sample size can be improved with future study. We give broadly applicable recommendations for future efforts, and with proper application, this approach could yield measurements in the quantities needed for robust statistical inference

Novel Serial Positive Enrichment Technology Enables Clinical Multiparameter Cell Sorting

A general obstacle for clinical cell preparations is limited purity, which causes variability in the quality and potency of cell products and might be responsible for negative side effects due to unwanted contaminants. Highly pure populations can be obtained best using positive selection techniques. However, in many cases target cell populations need to be segregated from other cells by combinations of multiple markers, which is still difficult to achieve – especially for clinical cell products. Therefore, we have generated low-affinity antibody-derived Fab-fragments, which stain like parental antibodies when multimerized via Strep-tag and Strep-Tactin, but can subsequently be removed entirely from the target cell population. Such reagents can be generated for virtually any antigen and can be used for sequential positive enrichment steps via paramagnetic beads. First protocols for multiparameter enrichment of two clinically relevant cell populations, CD4high/CD25high/CD45RAhigh ‘regulatory T cells’ and CD8high/CD62Lhigh/CD45RAneg ‘central memory T cells’, have been established to determine quality and efficacy parameters of this novel technology, which should have broad applicability for clinical cell sorting as well as basic research

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA

Strong gravitational lenses are a rare and instructive type of astronomical object. Identification has long relied on serendipity, but different strategies—such as mixed spectroscopy of multiple galaxies along the line of sight, machine-learning algorithms, and citizen science—have been employed to identify these objects as new imaging surveys become available. We report on the comparison between spectroscopic, machine-learning, and citizen-science identification of galaxy–galaxy lens candidates from independently constructed lens catalogs in the common survey area of the equatorial fields of the Galaxy and Mass Assembly survey. In these, we have the opportunity to compare high completeness spectroscopic identifications against high-fidelity imaging from the Kilo Degree Survey used for both machine-learning and citizen-science lens searches. We find that the three methods—spectroscopy, machine learning, and citizen science—identify 47, 47, and 13 candidates, respectively, in the 180 square degrees surveyed. These identifications barely overlap, with only two identified by both citizen science and machine learning. We have traced this discrepancy to inherent differences in the selection functions of each of the three methods, either within their parent samples (i.e., citizen science focuses on low redshift) or inherent to the method (i.e., machine learning is limited by its training sample and prefers well-separated features, while spectroscopy requires sufficient flux from lensed features to lie within the fiber). These differences manifest as separate samples in estimated Einstein radius, lens stellar mass, and lens redshift. The combined sample implies a lens candidate sky density of ~0.59 deg−2 and can inform the construction of a training set spanning a wider mass–redshift space. A combined approach and refinement of automated searches would result in a more complete sample of galaxy–galaxy lens candidates for future surveys

Multi-Virulence-Locus Sequence Typing of Listeria monocytogenes

A multi-virulence-locus sequence typing (MVLST) scheme was developed for subtyping Listeria monocytogenes, and the results obtained using this scheme were compared to those of pulsed-field gel electrophoresis (PFGE) and the published results of other typing methods, including ribotyping (RT) and multilocus sequence typing (MLST). A set of 28 strains (eight different serotypes and three known genetic lineages) of L. monocytogenes was selected from a strain collection (n > 1,000 strains) to represent the genetic diversity of this species. Internal fragments (ca. 418 to 469 bp) of three virulence genes (prfA, inlB, and inlC) and three virulence-associated genes (dal, lisR, and clpP) were sequenced and analyzed. Multiple DNA sequence alignment identified 10 (prfA), 19 (inlB), 13 (dal), 10 (lisR), 17 (inlC), and 16 (clpP) allelic types and a total of 28 unique sequence types. Comparison of MVLST with automated EcoRI-RT and PFGE with ApaI enzymatic digestion showed that MVLST was able to differentiate strains that were indistinguishable by RT (13 ribotypes; discrimination index = 0.921) or PFGE (22 profiles; discrimination index = 0.970). Comparison of MVLST with housekeeping-gene-based MLST analysis showed that MVLST provided higher discriminatory power for serotype 1/2a and 4b strains than MLST. Cluster analysis based on the intragenic sequences of the selected virulence genes indicated a strain phylogeny closely related to serotypes and genetic lineages. In conclusion, MVLST may improve the discriminatory power of MLST and provide a convenient tool for studying the local epidemiology of L. monocytogenes