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- Ackley, K., et al.
(author)
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Observational constraints on the optical and near-infrared emission from the neutron star-black hole binary merger candidate S190814bv
- 2020
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 643
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Journal article (peer-reviewed)abstract
- Context. Gravitational wave (GW) astronomy has rapidly reached maturity, becoming a fundamental observing window for modern astrophysics. The coalescences of a few tens of black hole (BH) binaries have been detected, while the number of events possibly including a neutron star (NS) is still limited to a few. On 2019 August 14, the LIGO and Virgo interferometers detected a high-significance event labelled S190814bv. A preliminary analysis of the GW data suggests that the event was likely due to the merger of a compact binary system formed by a BH and a NS.Aims. In this paper, we present our extensive search campaign aimed at uncovering the potential optical and near infrared electromagnetic counterpart of S190814bv. We found no convincing electromagnetic counterpart in our data. We therefore use our non-detection to place limits on the properties of the putative outflows that could have been produced by the binary during and after the merger.Methods. Thanks to the three-detector observation of S190814bv, and given the characteristics of the signal, the LIGO and Virgo Collaborations delivered a relatively narrow localisation in low latency - a 50% (90%) credible area of 5 deg(2) (23 deg(2)) - despite the relatively large distance of 26752 Mpc. ElectromagNetic counterparts of GRAvitational wave sources at the VEry Large Telescope collaboration members carried out an intensive multi-epoch, multi-instrument observational campaign to identify the possible optical and near infrared counterpart of the event. In addition, the ATLAS, GOTO, GRAWITA-VST, Pan-STARRS, and VINROUGE projects also carried out a search on this event. In this paper, we describe the combined observational campaign of these groups.Results. Our observations allow us to place limits on the presence of any counterpart and discuss the implications for the kilonova (KN), which was possibly generated by this NS-BH merger, and for the strategy of future searches. The typical depth of our wide-field observations, which cover most of the projected sky localisation probability (up to 99.8%, depending on the night and filter considered), is r similar to 22 (resp. K similar to 21) in the optical (resp. near infrared). We reach deeper limits in a subset of our galaxy-targeted observations, which cover a total similar to 50% of the galaxy-mass-weighted localisation probability. Altogether, our observations allow us to exclude a KN with large ejecta mass M greater than or similar to 0.1 M-circle dot to a high (> 90%) confidence, and we can exclude much smaller masses in a sub-sample of our observations. This disfavours the tidal disruption of the neutron star during the merger.Conclusions. Despite the sensitive instruments involved in the campaign, given the distance of S190814bv, we could not reach sufficiently deep limits to constrain a KN comparable in luminosity to AT 2017gfo on a large fraction of the localisation probability. This suggests that future (likely common) events at a few hundred megaparsecs will be detected only by large facilities with both a high sensitivity and large field of view. Galaxy-targeted observations can reach the needed depth over a relevant portion of the localisation probability with a smaller investment of resources, but the number of galaxies to be targeted in order to get a fairly complete coverage is large, even in the case of a localisation as good as that of this event.
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- Ntalla, Ioanna, et al.
(author)
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Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction
- 2020
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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Journal article (peer-reviewed)abstract
- The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N=293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease. On the electrocardiogram, the PR interval reflects conduction from the atria to ventricles and also serves as risk indicator of cardiovascular morbidity and mortality. Here, the authors perform genome-wide meta-analyses for PR interval in multiple ancestries and identify 141 previously unreported genetic loci.
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- Lynch, I, et al.
(author)
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Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?
- 2020
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In: Nanomaterials (Basel, Switzerland). - : MDPI AG. - 2079-4991. ; 10:12
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Journal article (peer-reviewed)abstract
- Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.
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- Wang, T, et al.
(author)
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Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders
- 2020
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 4932-
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Journal article (peer-reviewed)abstract
- Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case–control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E−06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E−07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype–genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.
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- Federico, A, et al.
(author)
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Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data
- 2020
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In: Nanomaterials (Basel, Switzerland). - : MDPI AG. - 2079-4991. ; 10:5
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Journal article (peer-reviewed)abstract
- Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.
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