Alcohol-related suicide across Australia : a geospatial analysis

Background: The acute effects of alcohol consumption are a major risk factor for suicide. Positive blood alcohol concentrations are present in almost one-third of all suicides at time of death. These suicides are defined as alcohol-related suicides. This cross-sectional study examines the geospatial distribution/clustering of high proportions of alcohol-related suicides and reports on socioeconomic and demographic risk factors. Methods: National Coronial Information System (NCIS) data were used to calculate proportions of suicides with alcohol present at the time of death for each level 3 statistical areas (SA3) in Australia. A density analysis and hotspot cluster analysis were used to visualise and establish statistically significant clustering of areas with higher (hotspots) and lower (coldspots) proportions. Subsequently, socioeconomic and demographic risk factors for alcohol use and suicide were reported on for hot and cold spots. Results: Significant clustering of areas with higher proportions of alcohol-related suicide occurred in northern Western Australia, the Northern Territory and Queensland, as well as inland New South Wales and inland Queensland. Clustering of SA3s with significantly lower proportions occurred in major city and inner regional Sydney and Melbourne. Conclusion and implications for public health: Results from this study identify areas in which prevention strategies should target alcohol use and can be used to inform prevention strategy design. Additionally, hotspots and coldspots identified in this study can be used for further analysis to better understand contextual risk factors for alcohol-related suicide

PlasmidTron: assembling the cause of phenotypes and genotypes from NGS data.

Increasingly rich metadata are now being linked to samples that have been whole-genome sequenced. However, much of this information is ignored. This is because linking this metadata to genes, or regions of the genome, usually relies on knowing the gene sequence(s) responsible for the particular trait being measured and looking for its presence or absence in that genome. Examples of this would be the spread of antimicrobial resistance genes carried on mobile genetic elements (MGEs). However, although it is possible to routinely identify the resistance gene, identifying the unknown MGE upon which it is carried can be much more difficult if the starting point is short-read whole-genome sequence data. The reason for this is that MGEs are often full of repeats and so assemble poorly, leading to fragmented consensus sequences. Since mobile DNA, which can carry many clinically and ecologically important genes, has a different evolutionary history from the host, its distribution across the host population will, by definition, be independent of the host phylogeny. It is possible to use this phenomenon in a genome-wide association study to identify both the genes associated with the specific trait and also the DNA linked to that gene, for example the flanking sequence of the plasmid vector on which it is encoded, which follows the same patterns of distribution as the marker gene/sequence itself. We present PlasmidTron, which utilizes the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographical information, to identify traits that are likely to be present on DNA that can randomly reassort across defined bacterial populations. It is also possible to use this methodology to associate unknown genes/sequences (e.g. plasmid backbones) with a specific molecular signature or marker (e.g. resistance gene presence or absence) using PlasmidTron. PlasmidTron uses a k-mer-based approach to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs in a fast and scalable-to-large manner. PlasmidTron is written in Python 3 and is available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron

PlasmidTron: assembling the cause of phenotypes from NGS data

AbstractWhen defining bacterial populations through whole genome sequencing (WGS) the samples often have detailed associated metadata that relate to disease severity, antimicrobial resistance, or even rare biochemical traits. When comparing these bacterial populations, it is apparent that some of these phenotypes do not follow the phylogeny of the host i.e. they are genetically unlinked to the evolutionary history of the host bacterium. One possible explanation for this phenomenon is that the genes are moving independently between hosts and are likely associated with mobile genetic elements (MGE). However, identifying the element that is associated with these traits can be complex if the starting point is short read WGS data. With the increased use of next generation WGS in routine diagnostics, surveillance and epidemiology a vast amount of short read data is available and these types of associations are relatively unexplored. One way to address this would be to perform assembly de novo of the whole genome read data, including its MGEs. However, MGEs are often full of repeats and can lead to fragmented consensus sequences. Deciding which sequence is part of the chromosome, and which is part of a MGE can be ambiguous. We present PlasmidTron, which utilises the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographic information, to identify sequences that are likely to represent MGEs linked to the phenotype. Given a set of reads, categorised into cases (showing the phenotype) and controls (phylogenetically related but phenotypically negative), PlasmidTron can be used to assemble de novo reads from each sample linked by a phenotype. A k-mer based analysis is performed to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs. By utilising k-mers and only assembling a fraction of the raw reads, the method is fast and scalable to large datasets. This approach has been tested on plasmids, because of their contribution to important pathogen associated traits, such as AMR, hence the name, but there is no reason why this approach cannot be utilized for any MGE that can move independently through a bacterial population. PlasmidTron is written in Python 3 and available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron.DATA SUMMARYSource code for PlasmidTron is available from Github under the open source licence GNU GPL 3; (url - https://goo.gl/ot6rT5)Simulated raw reads files have been deposited in Figshare; (url - https://doi.org/10.6084/m9.figshare.5406355.vl)Salmonella enterica serovar Weltevreden strain VNS10259 is available from GenBank; accession number GCA_001409135.Salmonella enterica serovar Typhi strain BL60006 is available from GenBank; accession number GCA_900185485.Accession numbers for all of the Illumina datasets used in this paper are listed in the supplementary tables.I/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. ⊠IMPACT STATEMENTPlasmidTron utilises the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographic information, to identify sequences that are likely to represent MGEs linked to the phenotype.</jats:sec

The Atacama Cosmology Telescope: the stellar content of galaxy clusters selected using the Sunyaev-Zel'dovich effect

We present a first measurement of the stellar mass component of galaxy clusters selected via the Sunyaev-Zel'dovich (SZ) effect, using 3.6 um and 4.5 um photometry from the Spitzer Space Telescope. Our sample consists of 14 clusters detected by the Atacama Cosmology Telescope (ACT), which span the redshift range 0.27 < z < 1.07 (median z = 0.50), and have dynamical mass measurements, accurate to about 30 per cent, with median M500 = 6.9 x 10^{14} MSun. We measure the 3.6 um and 4.5 um galaxy luminosity functions, finding the characteristic magnitude (m*) and faint-end slope (alpha) to be similar to those for IR-selected cluster samples. We perform the first measurements of the scaling of SZ-observables (Y500 and y0) with both brightest cluster galaxy (BCG) stellar mass and total cluster stellar mass (M500star). We find a significant correlation between BCG stellar mass and Y500 (E(z)^{-2/3} DA^2 Y500 ~ M*^{1.2 +/- 0.6}), although we are not able to obtain a strong constraint on the slope of the relation due to the small sample size. Additionally, we obtain E(z)^{-2/3} DA^2 Y500 ~ M500star^{1.0 +/- 0.6} for the scaling with total stellar mass. The mass fraction in stars spans the range 0.006-0.034, with the second ranked cluster in terms of dynamical mass (ACT-CL J0237-4939) having an unusually low total stellar mass and the lowest stellar mass fraction. For the five clusters with gas mass measurements available in the literature, we see no evidence for a shortfall of baryons relative to the cosmic mean value.Comment: Accepted for publication in MNRAS; 12 pages, 10 figure

Twin peaks: the Omicron SARS-CoV-2 BA.1 and BA.2 epidemics in England

BACKGROUND Rapid transmission of the SARS-CoV-2 Omicron variant has led to record-breaking incidence rates around the world. Sub-lineages have been detected in many countries with BA.1 replacing Delta and BA.2 replacing BA.1. METHODS The REal-time Assessment of Community Transmission-1 (REACT-1) study has tracked SARS-CoV-2 infection in England using RT-PCR results from self-administered throat and nose swabs from randomly-selected participants aged 5+ years. Rounds of data collection were approximately monthly from May 2020 to March 2022. RESULTS In March 2022, weighted prevalence was 6.37% (N=109,181), more than twice that in February 2022 following an initial Omicron peak in January 2022. Of the lineages determined by viral genome sequencing, 3,382 (99.97%) were Omicron, including 346 (10.2%) BA.1, 3035 (89.7%) BA.2 and one (0.03%) BA.3 sub-lineage; the remainder (1, 0.03%) was Delta AY.4. The BA.2 Omicron sub-lineage had a growth rate advantage (compared to BA.1 and sub-lineages) of 0.11 (95% credible interval [CrI], 0.10, 0.11). Prevalence was increasing overall (reproduction number R=1.07, 95% CrI, 1.06, 1.09), with the greatest increase in those aged 55+ years (R=1.12, 95% CrI, 1.09, 1.14) among whom estimated prevalence on March 31, 2022 was 8.31%, nearly 20-fold the median prevalence since May 1, 2020. CONCLUSIONS We observed unprecedented levels of SARS-CoV-2 infection in England in March 2022 and an almost complete replacement of Omicron BA.1 by BA.2. The high and increasing prevalence in older adults may increase hospitalizations and deaths despite high levels of vaccination. (Funded by the Department of Health and Social Care in England.

CoronaHiT: high-throughput sequencing of SARS-CoV-2 genomes.

We present CoronaHiT, a platform and throughput flexible method for sequencing SARS-CoV-2 genomes (≤ 96 on MinION or > 96 on Illumina NextSeq) depending on changing requirements experienced during the pandemic. CoronaHiT uses transposase-based library preparation of ARTIC PCR products. Method performance was demonstrated by sequencing 2 plates containing 95 and 59 SARS-CoV-2 genomes on nanopore and Illumina platforms and comparing to the ARTIC LoCost nanopore method. Of the 154 samples sequenced using all 3 methods, ≥ 90% genome coverage was obtained for 64.3% using ARTIC LoCost, 71.4% using CoronaHiT-ONT and 76.6% using CoronaHiT-Illumina, with almost identical clustering on a maximum likelihood tree. This protocol will aid the rapid expansion of SARS-CoV-2 genome sequencing globally.The sequencing costs were funded by the COVID-19 Genomics UK (COG-UK) Consortium which is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute

A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation

Deep brain stimulation (DBS) is an established therapy for Parkinson's Disease and is being investigated as a treatment for chronic depression, obsessive compulsive disorder and for facilitating functional recovery of patients in minimally conscious states following brain injury. For all of these applications, quantitative assessments of the behavioral effects of DBS are crucial to determine whether the therapy is effective and, if so, how stimulation parameters can be optimized. Behavioral analyses for DBS are challenging because subject performance is typically assessed from only a small set of discrete measurements made on a discrete rating scale, the time course of DBS effects is unknown, and between-subject differences are often large. We demonstrate how Bayesian state-space methods can be used to characterize the relationship between DBS and behavior comparing our approach with logistic regression in two experiments: the effects of DBS on attention of a macaque monkey performing a reaction-time task, and the effects of DBS on motor behavior of a human patient in a minimally conscious state. The state-space analysis can assess the magnitude of DBS behavioral facilitation (positive or negative) at specific time points and has important implications for developing principled strategies to optimize DBS paradigms.National Institutes of Health (U.S.)(R01 MH-071847)National Institutes of Health (U.S.) (DP1 OD003646)National Institutes of Health (U.S.)(NS02172)IntElect Medical (Firm

Efficacy of Losartan in Hospitalized Patients With COVID-19-Induced Lung Injury: A Randomized Clinical Trial

Importance: SARS-CoV-2 viral entry may disrupt angiotensin II (AII) homeostasis, contributing to COVID-19 induced lung injury. AII type 1 receptor blockade mitigates lung injury in preclinical models, although data in humans with COVID-19 remain mixed. Objective: To test the efficacy of losartan to reduce lung injury in hospitalized patients with COVID-19. Design, Setting, and Participants: This blinded, placebo-controlled randomized clinical trial was conducted in 13 hospitals in the United States from April 2020 to February 2021. Hospitalized patients with COVID-19 and a respiratory sequential organ failure assessment score of at least 1 and not already using a renin-angiotensin-aldosterone system (RAAS) inhibitor were eligible for participation. Data were analyzed from April 19 to August 24, 2021. Interventions: Losartan 50 mg orally twice daily vs equivalent placebo for 10 days or until hospital discharge. Main Outcomes and Measures: The primary outcome was the imputed arterial partial pressure of oxygen to fraction of inspired oxygen (Pao2:Fio2) ratio at 7 days. Secondary outcomes included ordinal COVID-19 severity; days without supplemental o2, ventilation, or vasopressors; and mortality. Losartan pharmacokinetics and RAAS components (AII, angiotensin-[1-7] and angiotensin-converting enzymes 1 and 2)] were measured in a subgroup of participants. Results: A total of 205 participants (mean [SD] age, 55.2 [15.7] years; 123 [60.0%] men) were randomized, with 101 participants assigned to losartan and 104 participants assigned to placebo. Compared with placebo, losartan did not significantly affect Pao2:Fio2 ratio at 7 days (difference, -24.8 [95%, -55.6 to 6.1]; P = .12). Compared with placebo, losartan did not improve any secondary clinical outcomes and led to fewer vasopressor-free days than placebo (median [IQR], 9.4 [9.1-9.8] vasopressor-free days vs 8.7 [8.2-9.3] vasopressor-free days). Conclusions and Relevance: This randomized clinical trial found that initiation of orally administered losartan to hospitalized patients with COVID-19 and acute lung injury did not improve Pao2:Fio2 ratio at 7 days. These data may have implications for ongoing clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT04312009