Deuterium Abundance in the Most Metal-Poor Damped Lyman alpha System: Converging on Omega_baryons

The most metal-poor DLA known to date, at z = 2.61843 in the spectrum of the QSO Q0913+072, with an oxygen abundance only about 1/250 of the solar value, shows six well resolved D I Lyman series transitions in high quality echelle spectra recently obtained with the ESO VLT. We deduce a value of the deuterium abundance log (D/H) = -4.56+/-0.04 which is in good agreement with four out of the six most reliable previous determinations of this ratio in QSO absorbers. We find plausible reasons why in the other two cases the 1 sigma errors may have been underestimated by about a factor of two. The addition of this latest data point does not change significantly the mean value of the primordial abundance of deuterium, suggesting that we are now converging to a reliable measure of this quantity. We conclude that = -4.55+/-0.03 and Omega_b h^2 (BBN) = 0.0213+/-0.0010 (68% confidence limits). Including the latter as a prior in the analysis of the five year data of WMAP leads to a revised best-fitting value of the power-law index of primordial fluctuations n_s = 0.956+/-0.013 (1 sigma) and n_s < 0.990 with 99% confidence. Considering together the constraints provided by WMAP 5, (D/H)_p, baryon oscillations in the galaxy distribution, and distances to Type Ia supernovae, we arrive at the current best estimates Omega_b h^2 = 0.0224+/-0.0005 and n_s = 0.959+/-0.013.Comment: 13 pages, 8 Figures. Revised version following referee's comments. Accepted for publication in Monthly Notices of the Royal Astronomical Society. A few typos correcte

Beam mismatch effects in Cosmic Microwave Background polarization measurements

Measurement of cosmic microwave background polarization is today a major goal of observational cosmology. The level of the signal to measure, however, makes it very sensitive to various systematic effects. In the case of Planck, which measures polarization by combining data from various detectors, the beam asymmetry can induce a temperature leakage or a polarization mode mixing. In this paper, we investigate this effect using realistic simulated beams and propose a first-order method to correct the polarization power spectra for the induced systematic effect.Comment: Accepted by Astronomy & Astrophysic

The effect of hypoglycaemia during hospital admission on health-related outcomes for people with diabetes: a systematic review and meta-analysis

Aims: To assess the health-related outcomes of hypoglycaemia for people with diabetes admitted to hospital; specifically, hospital length of stay and mortality.Methods: We conducted a systematic review and meta-analysis of studies relating to inpatient hypoglycaemia (<4 mmol/L) for hospitalised adults (≥16 years) with diabetes reporting the primary outcomes of interest, hospital length of stay or mortality. Final papers for inclusion were reviewed in duplicate and the adjusted results of each were pooled, using a random effects model then undergoing further prespecified subgroup analysis.Results: 15 studies were included in the meta-analysis. The pooled mean difference in length of stay for ward-based inpatients exposed to hypoglycaemia was 4.1 days longer (95% confidence interval [CI], 2.36-5.79; IÇ = 99%) compared to inpatients without hypoglycaemia. This association remained robust across the pre-specified subgroup analyses. The pooled relative risk (RR) of in-hospital mortality was greater for inpatients exposed to hypoglycaemia 2.09 (95% CI, 1.64 to 2.67; IÇ = 94%, n=7 studies) but not in intensive care unit mortality RR 0.75 (0.49 to 1.16; IÇ =0%, n=2 studies).Conclusion: There is an association between inpatient hypoglycaemia and longer length of stay and greater in-hospital mortality. Studies examining this association were heterogenous in terms of both clinical populations and effect size, but the overall direction of the association was consistent. Therefore, glucose concentration should be considered a potential tool to aid the identification of patients at risk of poor health-related outcomes

Recurrent neural network equalizer to extend input power dynamic range of SOA in 100Gb/s/λ PON

We propose a novel equalization scheme for 100Gb/s/λ PAM4 PON based on Gated Recurrent Neural Network to increase SOA preamplifier input power dynamic range tolerance to 30 dB below hard-decision FEC BER limit of 3.8×10 −3

High dynamic range 100 Gbit/s PAM4 PON with SOA preamplifier using Gated Recurrent Neural Network equaliser

We investigate parallel multi-symbol equalisation scheme for 100Gb/s/λ PAM4 using Gated Recurrent Neural Networks and exploit SOA preamplifier gain suppression to achieve 27 dB system dynamic range below hard-decision FEC BER limit of 3.8 × 10−3 using a receiver with two gain settings

Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery

Background: Blood transfusion can be a lifesaving intervention after perioperative blood loss. Many prediction models have been developed to identify patients most likely to require blood transfusion during elective surgery, but it is unclear whether any are suitable for clinical practice. Study Design and Setting: We conducted a systematic review, searching MEDLINE, Embase, PubMed, The Cochrane Library, Transfusion Evidence Library, Scopus, and Web of Science databases for studies reporting the development or validation of a blood transfusion prediction model in elective surgery patients between January 1, 2000 and June 30, 2021. We extracted study characteristics, discrimination performance (c-statistics) of final models, and data, which we used to perform risk of bias assessment using the Prediction model risk of bias assessment tool (PROBAST). Results: We reviewed 66 studies (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes. Conclusion: Most blood transfusion prediction models are at high risk of bias and suffer from poor reporting and methodological quality, which must be addressed before they can be safely used in clinical practice

Variations in COVID-19 vaccination uptake among people in receipt of psychotropic drugs: cross-sectional analysis of a national population-based prospective cohort

BackgroundCoronavirus disease 2019 (COVID-19) has disproportionately affected people with mental health conditions.AimsWe investigated the association between receiving psychotropic drugs, as an indicator of mental health conditions, and COVID-19 vaccine uptake.MethodWe conducted a cross-sectional analysis of a prospective cohort of the Northern Ireland adult population using national linked primary care registration, vaccination, secondary care and pharmacy dispensing data. Univariable and multivariable logistic regression analyses investigated the association between anxiolytic, antidepressant, antipsychotic, and hypnotic use and COVID-19 vaccination status, accounting for age, gender, deprivation and comorbidities. Receiving any COVID-19 vaccine was the primary outcome.ResultsThere were 1 433 814 individuals, of whom 1 166 917 received a COVID-19 vaccination. Psychotropic medications were dispensed to 267 049 people. In univariable analysis, people who received any psychotropic medication had greater odds of receiving COVID-19 vaccination: odds ratio (OR) = 1.42 (95% CI 1.41–1.44). However, after adjustment, psychotropic medication use was associated with reduced odds of vaccination (ORadj = 0.90, 95% CI 0.89–0.91). People who received anxiolytics (ORadj = 0.63, 95% CI 0.61–0.65), antipsychotics (ORadj = 0.75, 95% CI 0.73–0.78) and hypnotics (ORadj = 0.90, 95% CI 0.87–0.93) had reduced odds of being vaccinated. Antidepressant use was not associated with vaccination (ORadj = 1.02, 95% CI 1.00–1.03).ConclusionsWe found significantly lower odds of vaccination in people who were receiving treatment with anxiolytic and antipsychotic medications. There is an urgent need for evidence-based, tailored vaccine support for people with mental health conditions

FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ