Systematic and statistical errors in a Bayesian approach to the estimation of the neutron-star equation of state using advanced gravitational wave detectors

Advanced ground-based gravitational-wave detectors are capable of measuring tidal influences in binary neutron-star systems. In this work, we report on the statistical uncertainties in measuring tidal deformability with a full Bayesian parameter estimation implementation. We show how simultaneous measurements of chirp mass and tidal deformability can be used to constrain the neutron-star equation of state. We also study the effects of waveform modeling bias and individual instances of detector noise on these measurements. We notably find that systematic error between post-Newtonian waveform families can significantly bias the estimation of tidal parameters, thus motivating the continued development of waveform models that are more reliable at high frequencies

The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO's Second and Advanced Virgo's First Observing Runs

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method.Comment: 12 pages, 7 figures, to be submitted to Phys. Rev. D, comments welcom

Genome-Wide Association Studies in Dogs and Humans Identify ADAMTS20 as a Risk Variant for Cleft Lip and Palate

Cleft lip with or without cleft palate (CL/P) is the most commonly occurring craniofacial birth defect. We provide insight into the genetic etiology of this birth defect by performing genome-wide association studies in two species: dogs and humans. In the dog, a genome-wide association study of 7 CL/P cases and 112 controls from the Nova Scotia Duck Tolling Retriever (NSDTR) breed identified a significantly associated region on canine chromosome 27 (unadjusted p=1.1 x 10-13; adjusted p= 2.2 x 10-3). Further analysis in NSDTR families and additional full sibling cases identified a 1.44 Mb homozygous haplotype (chromosome 27: 9.29 – 10.73 Mb) segregating with a more complex phenotype of cleft lip, cleft palate, and syndactyly (CLPS) in 13 cases. Whole-genome sequencing of 3 CLPS cases and 4 controls at 15X coverage led to the discovery of a frameshift mutation within ADAMTS20 (c.1360_1361delAA (p.Lys453Ilefs*3)), which segregated concordant with the phenotype. In a parallel study in humans, a family-based association analysis (DFAM) of 125 CL/P cases, 420 unaffected relatives, and 392 controls from a Guatemalan cohort, identified a suggestive association (rs10785430; p =2.67 x 10-6) with the same gene, ADAMTS20. Sequencing of cases from the Guatemalan cohort was unable to identify a causative mutation within the coding region of ADAMTS20, but four coding variants were found in additional cases of CL/P. In summary, this study provides genetic evidence for a role of ADAMTS20 in CL/P development in dogs and as a candidate gene for CL/P development in humans

Identification and quantification of microplastics in wastewater using focal plane array-based reflectance micro-FT-IR imaging

Microplastics (<5 mm) have been documented in environmental samples on a global scale. While these pollutants may enter aquatic environments via wastewater treatment facilities, the abundance of microplastics in these matrices has not been investigated. Although efficient methods for the analysis of microplastics in sediment samples and marine organisms have been published, no methods have been developed for detecting these pollutants within organic-rich wastewater samples. In addition, there is no standardized method for analyzing microplastics isolated from environmental samples. In many cases, part of the identification protocol relies on visual selection before analysis, which is open to bias. In order to address this, a new method for the analysis of microplastics in wastewater was developed. A pretreatment step using 30% hydrogen peroxide (H2O2) was employed to remove biogenic material, and focal plane array (FPA)-based reflectance micro-Fourier-transform (FT-IR) imaging was shown to successfully image and identify different microplastic types (polyethylene, polypropylene, nylon-6, polyvinyl chloride, polystyrene). Microplastic-spiked wastewater samples were used to validate the methodology, resulting in a robust protocol which was nonselective and reproducible (the overall success identification rate was 98.33%). The use of FPA-based micro-FT-IR spectroscopy also provides a considerable reduction in analysis time compared with previous methods, since samples that could take several days to be mapped using a single-element detector can now be imaged in less than 9 h (circular filter with a diameter of 47 mm). This method for identifying and quantifying microplastics in wastewater is likely to provide an essential tool for further research into the pathways by which microplastics enter the environment.This work is funded by a NERC (Natural Environment Research Council) CASE studentship (NE/K007521/1) with contribution from industrial partner Fera Science Ltd., United Kingdom. The authors would like to thank Peter Vale, from Severn Trent Water Ltd, for providing access to and additionally Ashley Howkins (Brunel University London) for providing travel and assistance with the sampling of the Severn Trent wastewater treatment plant in Derbyshire, UK. We are grateful to Emma Bradley and Chris Sinclair for providing helpful suggestions for our research