Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study

Background Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors. Methods Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration. Results We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79–0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76–0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74–0.88] and KCH AUROC 0.72 [0.68–0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration. Conclusions The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease

Entanglement-enhanced probing of a delicate material system

Quantum metrology uses entanglement and other quantum effects to improve the sensitivity of demanding measurements. Probing of delicate systems demands high sensitivity from limited probe energy and has motivated the field's key benchmark-the standard quantum limit. Here we report the first entanglement-enhanced measurement of a delicate material system. We non-destructively probe an atomic spin ensemble by means of near-resonant Faraday rotation, a measurement that is limited by probe-induced scattering in quantum-memory and spin-squeezing applications. We use narrowband, atom-resonant NOON states to beat the standard quantum limit of sensitivity by more than five standard deviations, both on a per-photon and per-damage basis. This demonstrates quantum enhancement with fully realistic loss and noise, including variable-loss effects. The experiment opens the way to ultra-gentle probing of single atoms, single molecules, quantum gases and living cells.Comment: 7 pages, 8 figures; Nature Photonics, advance online publication, 16 December 201

Vortices in Superfluid Films on Curved Surfaces

We present a systematic study of how vortices in superfluid films interact with the spatially varying Gaussian curvature of the underlying substrate. The Gaussian curvature acts as a source for a geometric potential that attracts (repels) vortices towards regions of negative (positive) Gaussian curvature independently of the sign of their topological charge. Various experimental tests involving rotating superfluid films and vortex pinning are first discussed for films coating gently curved substrates that can be treated in perturbation theory from flatness. An estimate of the experimental regimes of interest is obtained by comparing the strength of the geometrical forces to the vortex pinning induced by the varying thickness of the film which is in turn caused by capillary effects and gravity. We then present a non-perturbative technique based on conformal mappings that leads an exact solution for the geometric potential as well as the geometric correction to the interaction between vortices. The conformal mapping approach is illustrated by means of explicit calculations of the geometric effects encountered in the study of some strongly curved surfaces and by deriving universal bounds on their strength.Comment: 50 pages, 38 figure

A next generation vaccine against human rabies based on a single dose of a chimpanzee adenovirus vector serotype C

Rabies, caused by RNA viruses in the Genus Lyssavirus, is the most fatal of all infectious diseases. This neglected zoonosis remains a major public health problem in developing countries, causing the death of an estimated 25,000-159,000 people each year, with more than half of them in children. The high incidence of human rabies in spite of effective vaccines is mainly linked to the lack of compliance with the complicated administration schedule, inadequacies of the community public health system for local administration by the parenteral route and the overall costs of the vaccine. The goal of our work was the development of a simple, affordable and effective vaccine strategy to prevent human rabies virus infection. This next generation vaccine is based on a replication-defective chimpanzee adenovirus vector belonging to group C, ChAd155-RG, which encodes the rabies glycoprotein (G). We demonstrate here that a single dose of this vaccine induces protective efficacy in a murine model of rabies challenge and elicits strong and durable neutralizing antibody responses in vaccinated non-human primates. Importantly, we demonstrate that one dose of a commercial rabies vaccine effectively boosts the neutralizing antibody responses induced by ChAd155-RG in vaccinated monkeys, showing the compatibility of the novel vectored vaccine with the current post-exposure prophylaxis in the event of rabies virus exposure. Finally, we demonstrate that antibodies induced by ChAd155-RG can also neutralize European bat lyssaviruses 1 and 2 (EBLV-1 and EBLV-2) found in bat reservoirs

Discovering Sequence Motifs with Arbitrary Insertions and Deletions

Biology is encoded in molecular sequences: deciphering this encoding remains a grand scientific challenge. Functional regions of DNA, RNA, and protein sequences often exhibit characteristic but subtle motifs; thus, computational discovery of motifs in sequences is a fundamental and much-studied problem. However, most current algorithms do not allow for insertions or deletions (indels) within motifs, and the few that do have other limitations. We present a method, GLAM2 (Gapped Local Alignment of Motifs), for discovering motifs allowing indels in a fully general manner, and a companion method GLAM2SCAN for searching sequence databases using such motifs. glam2 is a generalization of the gapless Gibbs sampling algorithm. It re-discovers variable-width protein motifs from the PROSITE database significantly more accurately than the alternative methods PRATT and SAM-T2K. Furthermore, it usefully refines protein motifs from the ELM database: in some cases, the refined motifs make orders of magnitude fewer overpredictions than the original ELM regular expressions. GLAM2 performs respectably on the BAliBASE multiple alignment benchmark, and may be superior to leading multiple alignment methods for “motif-like” alignments with N- and C-terminal extensions. Finally, we demonstrate the use of GLAM2 to discover protein kinase substrate motifs and a gapped DNA motif for the LIM-only transcriptional regulatory complex: using GLAM2SCAN, we identify promising targets for the latter. GLAM2 is especially promising for short protein motifs, and it should improve our ability to identify the protein cleavage sites, interaction sites, post-translational modification attachment sites, etc., that underlie much of biology. It may be equally useful for arbitrarily gapped motifs in DNA and RNA, although fewer examples of such motifs are known at present. GLAM2 is public domain software, available for download at http://bioinformatics.org.au/glam2