Protocol for the Smoking, Nicotine and Pregnancy (SNAP) trial: double-blind, placebo-randomised, controlled trial of nicotine replacement therapy in pregnancy

Background: Smoking in pregnancy remains a public health challenge. Nicotine replacement therapy (NRT) is effective for smoking cessation in non-pregnant people, but because women metabolise nicotine and cotinine much faster in pregnancy, it is unclear whether this will be effective for smoking cessation in pregnancy. The NHS Health Technology Assessment Programme (HTA)-funded smoking, nicotine and pregnancy ( SNAP) trial will investigate whether or not nicotine replacement therapy ( NRT) is effective, cost-effective and safe when used for smoking cessation by pregnant women. Methods/Design: Over two years, in 5 trial centres, 1050 pregnant women who are between 12 and 24 weeks pregnant will be randomised as they attend hospital for ante-natal ultrasound scans. Women will receive either nicotine or placebo transdermal patches with behavioural support. The primary outcome measure is biochemically-validated, self-reported, prolonged and total abstinence from smoking between a quit date ( defined before randomisation and set within two weeks of this) and delivery. At six months after childbirth self-reported maternal smoking status will be ascertained and two years after childbirth, self-reported maternal smoking status and the behaviour, cognitive development and respiratory symptoms of children born in the trial will be compared in both groups. Discussion: This trial is designed to ascertain whether or not standard doses of NRT ( as transdermal patches) are effective and safe when used for smoking cessation during pregnancy

Murine model for Fusarium oxysporum invasive fusariosis reveals organ-specific structures for dissemination and long-term persistence

Peer reviewedPublisher PD

Genome-Wide Mutagenesis of Xanthomonas axonopodis pv. citri Reveals Novel Genetic Determinants and Regulation Mechanisms of Biofilm Formation

Xanthomonas axonopodis pv. citri (Xac) causes citrus canker disease, a major threat to citrus production worldwide. Accumulating evidence suggests that the formation of biofilms on citrus leaves plays an important role in the epiphytic survival of this pathogen prior to the development of canker disease. However, the process of Xac biofilm formation is poorly understood. Here, we report a genome-scale study of Xac biofilm formation in which we identified 92 genes, including 33 novel genes involved in biofilm formation and 7 previously characterized genes, colR, fhaB, fliC, galU, gumD, wxacO, and rbfC, known to be important for Xac biofilm formation. In addition, 52 other genes with defined or putative functions in biofilm formation were identified, even though they had not previously reported been to be associated with biofilm formation. The 92 genes were isolated from 292 biofilm-defective mutants following a screen of a transposon insertion library containing 22,000 Xac strain 306 mutants. Further analyses indicated that 16 of the novel genes are involved in the production of extracellular polysaccharide (EPS) and/or lipopolysaccharide (LPS), 7 genes are involved in signaling and regulatory pathways, and 5 genes have unknown roles in biofilm formation. Furthermore, two novel genes, XAC0482, encoding a haloacid dehalogenase-like phosphatase, and XAC0494 (designated as rbfS), encoding a two-component sensor protein, were confirmed to be biofilm-related genes through complementation assays. Our data demonstrate that the formation of mature biofilm requires EPS, LPS, both flagellum-dependent and flagellum-independent cell motility, secreted proteins and extracellular DNA. Additionally, multiple signaling pathways are involved in Xac biofilm formation. This work is the first report on a genome-wide scale of the genetic processes of biofilm formation in plant pathogenic bacteria. The report provides significant new information about the genetic determinants and regulatory mechanism of biofilm formation

Search for gravitational waves associated with gamma-ray bursts detected by Fermi and Swift during the LIGO–Virgo run O3b

We search for gravitational-wave signals associated with gamma-ray bursts (GRBs) detected by the Fermi and Swift satellites during the second half of the third observing run of Advanced LIGO and Advanced Virgo (2019 November 1 15:00 UTC–2020 March 27 17:00 UTC). We conduct two independent searches: a generic gravitational-wave transients search to analyze 86 GRBs and an analysis to target binary mergers with at least one neutron star as short GRB progenitors for 17 events. We find no significant evidence for gravitational-wave signals associated with any of these GRBs. A weighted binomial test of the combined results finds no evidence for subthreshold gravitational-wave signals associated with this GRB ensemble either. We use several source types and signal morphologies during the searches, resulting in lower bounds on the estimated distance to each GRB. Finally, we constrain the population of low-luminosity short GRBs using results from the first to the third observing runs of Advanced LIGO and Advanced Virgo. The resulting population is in accordance with the local binary neutron star merger rate

Multi-level emulation of complex climate model responses to boundary forcing data

Climate model components involve both high-dimensional input and output fields. It is desirable to e ciently generate spatio-temporal out-puts of these models for applications in integrated assessment modelling or to assess the statistical relationship between such sets of inputs and outputs, for example, uncertainty analysis. However, the need for e ciency often compromises the fidelity of output through the use of low complexity models. Here, we develop a technique which combines statistical emulation with a dimensionality reduction technique to emulate a wide range of outputs from an atmospheric general circulation model, PLASIM, as functions of the boundary forcing prescribed by the ocean component of a lower complexity climate model, GENIE-1. Although accurate and detailed spatial information on atmospheric variables such as precipitation and wind speed is well beyond the capability of GENIE-1’s energy-moisture balance model of the atmosphere, this study demonstrates that the output of this model is useful in predicting PLASIM’s spatio-temporal fields through multi-level emulation. Meaningful information from the fast model, GENIE-1 was extracted by utilising the correlation between variables of the same type in the two models and between variables of di↵erent types in PLASIM. We present here the construction and validation of several PLASIM variable emulators and discuss their potential use in developing a hybrid model with statistical components

Prolactin induces adrenal hypertrophy

Although adrenocorticotropic hormone is generally considered to play a major role in the regulation of adrenal glucocorticoid secretion, several reports have suggested that other pituitary hormones (e.g., prolactin) also play a significant role in the regulation of adrenal function. The aim of the present study was to measure the adrenocortical cell area and to determine the effects of the transition from the prepubertal to the postpubertal period on the hyperprolactinemic state induced by domperidone (4.0 mg kg-1 day-1, sc). In hyperprolactinemic adult and young rats, the adrenals were heavier, as determined at necropsy, than in the respective controls: adults (30 days: 0.16 ± 0.008 and 0.11 ± 0.007; 46 days: 0.17 ± 0.006 and 0.12 ± 0.008, and 61 days: 0.17 ± 0.008 and 0.10 ± 0.004 mg for treated and control animals, respectively; P < 0.05), and young rats (30 days: 0.19 ± 0.003 and 0.16 ± 0.007, and 60 days: 0.16 ± 0.006 and 0.13 ± 0.009 mg; P < 0.05). We selected randomly a circular area in which we counted the nuclei of adrenocortical cells. The area of zona fasciculata cells was increased in hyperprolactinemic adult and young rats compared to controls: adults: (61 days: 524.90 ± 47.85 and 244.84 ± 9.03 µm² for treated and control animals, respectively; P < 0.05), and young rats: (15 days: 462.30 ± 16.24 and 414.28 ± 18.19; 60 days: 640.51 ± 12.91 and 480.24 ± 22.79 µm²; P < 0.05). Based on these data we conclude that the increase in adrenal weight observed in the hyperprolactinemic animals may be due to prolactin-induced adrenocortical cell hypertrophy