Evaluating auroral forecasts against satellite observations

The aurora is a readily visible phenomenon of interest to many members of the public. However, the aurora and associated phenomena can also significantly impact communications, ground-based infrastructure, and high-altitude radiation exposure. Forecasting the location of the auroral oval is therefore a key component of space weather forecast operations. A version of the OVATION-Prime 2013 auroral precipitation model (Newell et al., 2014, https://doi.org/10.1002/2014sw001056) was used by the UK Met Office Space Weather Operations Centre (MOSWOC). The operational implementation of the OVATION-Prime 2013 model at the UK Met Office delivered a 30-min forecast of the location of the auroral oval and the probability of observing the aurora. Using weather forecast evaluation techniques, we evaluate the ability of the OVATION-Prime 2013 model forecasts to predict the location and probability of the aurora occurring by comparing the forecasts with auroral boundaries determined from data from the IMAGE satellite between 2000 and 2002. Our analysis shows that the operational model performs well at predicting the location of the auroral oval, with a relative operating characteristic (ROC) score of 0.82. The model performance is reduced in the dayside local time sectors (ROC score = 0.59) and during periods of higher geomagnetic activity (ROC score of 0.55 for Kp = 8). As a probabilistic forecast, OVATION-Prime 2013 tends to underpredict the occurrence of aurora by a factor of 1.1–6, while probabilities of over 90% are overpredicted

The Taming of Closed Time-like Curves

We consider a $R^{1,d}/Z_2$ orbifold, where $Z_2$ acts by time and space reversal, also known as the embedding space of the elliptic de Sitter space. The background has two potentially dangerous problems: time-nonorientability and the existence of closed time-like curves. We first show that closed causal curves disappear after a proper definition of the time function. We then consider the one-loop vacuum expectation value of the stress tensor. A naive QFT analysis yields a divergent result. We then analyze the stress tensor in bosonic string theory, and find the same result as if the target space would be just the Minkowski space $R^{1,d}$, suggesting a zero result for the superstring. This leads us to propose a proper reformulation of QFT, and recalculate the stress tensor. We find almost the same result as in Minkowski space, except for a potential divergence at the initial time slice of the orbifold, analogous to a spacelike Big Bang singularity. Finally, we argue that it is possible to define local S-matrices, even if the spacetime is globally time-nonorientable.Comment: 37 pages, LaTeX2e, uses amssymb, amsmath and epsf macros, 8 eps and 3 ps figures; (v2): Two additional comments + one reference added; (v3): corrections in discussion of CTCs + some clarification

A comparative study of epitaxial InGaAsBi/InP structures using Rutherford backscattering spectrometry, X-ray diffraction and photoluminescence techniques

In this work, we used a combination of photoluminescence (PL), high resolution X-ray diffraction (XRD), and Rutherford backscattering spectrometry (RBS) techniques to investigate material quality and structural properties of MBE-grown InGaAsBi samples (with and without an InGaAs cap layer) with targeted bismuth composition in the 3%–4% range. XRD data showed that the InGaAsBi layers are more homogeneous in the uncapped samples. For the capped samples, the growth of the InGaAs capped layer at higher temperature affects the quality of the InGaAsBi layer and bismuth distribution in the growth direction. Low-temperature PL exhibited multiple emission peaks; the peak energies, widths, and relative intensities were used for comparative analysis of the data in line with the XRD and RBS results. RBS data at a random orientation together with channeled measurements allowed both an estimation of the bismuth composition and analysis of the structural properties. The RBS channeling showed evidence of higher strain due to possible antisite defects in the capped samples grown at a higher temperature. It is also suggested that the growth of the capped layer at high temperature causes deterioration of the bismuth-layer quality. The RBS analysis demonstrated evidence of a reduction of homogeneity of uncapped InGaAsBi layers with increasing bismuth concentration. The uncapped higher bismuth concentration sample showed less defined channeling dips suggesting poorer crystal quality and clustering of bismuth on the sample surface

A comparison of flare forecasting methods. II. Benchmarks, metrics and performance results for operational solar flare forecasting systems

YesSolar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology (Barnes et al. 2016, Paper I), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to – for the first time – directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the “no skill” level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series we ask why the performances differ by examining implementation details (Leka et al. 2019, Paper III), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in (Park et al. 2019, Paper IV). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks, and today’s performance benchmarks against which improvements and new methods may be compared

Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study

Background: Sleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea. Methods: CircCOVID was a prospective multicentre cohort substudy designed to investigate the effects of circadian disruption and sleep disturbance on recovery after COVID-19 in a cohort of participants aged 18 years or older, admitted to hospital for COVID-19 in the UK, and discharged between March, 2020, and October, 2021. Participants were recruited from the Post-hospitalisation COVID-19 study (PHOSP-COVID). Follow-up data were collected at two timepoints: an early time point 2–7 months after hospital discharge and a later time point 10–14 months after hospital discharge. Sleep quality was assessed subjectively using the Pittsburgh Sleep Quality Index questionnaire and a numerical rating scale. Sleep quality was also assessed with an accelerometer worn on the wrist (actigraphy) for 14 days. Participants were also clinically phenotyped, including assessment of symptoms (ie, anxiety [Generalised Anxiety Disorder 7-item scale questionnaire], muscle function [SARC-F questionnaire], dyspnoea [Dyspnoea-12 questionnaire] and measurement of lung function), at the early timepoint after discharge. Actigraphy results were also compared to a matched UK Biobank cohort (non-hospitalised individuals and recently hospitalised individuals). Multivariable linear regression was used to define associations of sleep disturbance with the primary outcome of breathlessness and the other clinical symptoms. PHOSP-COVID is registered on the ISRCTN Registry (ISRCTN10980107). Findings: 2320 of 2468 participants in the PHOSP-COVID study attended an early timepoint research visit a median of 5 months (IQR 4–6) following discharge from 83 hospitals in the UK. Data for sleep quality were assessed by subjective measures (the Pittsburgh Sleep Quality Index questionnaire and the numerical rating scale) for 638 participants at the early time point. Sleep quality was also assessed using device-based measures (actigraphy) a median of 7 months (IQR 5–8 months) after discharge from hospital for 729 participants. After discharge from hospital, the majority (396 [62%] of 638) of participants who had been admitted to hospital for COVID-19 reported poor sleep quality in response to the Pittsburgh Sleep Quality Index questionnaire. A comparable proportion (338 [53%] of 638) of participants felt their sleep quality had deteriorated following discharge after COVID-19 admission, as assessed by the numerical rating scale. Device-based measurements were compared to an age-matched, sex-matched, BMI-matched, and time from discharge-matched UK Biobank cohort who had recently been admitted to hospital. Compared to the recently hospitalised matched UK Biobank cohort, participants in our study slept on average 65 min (95% CI 59 to 71) longer, had a lower sleep regularity index (–19%; 95% CI –20 to –16), and a lower sleep efficiency (3·83 percentage points; 95% CI 3·40 to 4·26). Similar results were obtained when comparisons were made with the non-hospitalised UK Biobank cohort. Overall sleep quality (unadjusted effect estimate 3·94; 95% CI 2·78 to 5·10), deterioration in sleep quality following hospital admission (3·00; 1·82 to 4·28), and sleep regularity (4·38; 2·10 to 6·65) were associated with higher dyspnoea scores. Poor sleep quality, deterioration in sleep quality, and sleep regularity were also associated with impaired lung function, as assessed by forced vital capacity. Depending on the sleep metric, anxiety mediated 18–39% of the effect of sleep disturbance on dyspnoea, while muscle weakness mediated 27–41% of this effect. Interpretation: Sleep disturbance following hospital admission for COVID-19 is associated with dyspnoea, anxiety, and muscle weakness. Due to the association with multiple symptoms, targeting sleep disturbance might be beneficial in treating the post-COVID-19 condition. Funding: UK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council

SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

BACKGROUND: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Pain severity predicts depressive symptoms over and above individual illnesses and multimorbidity in older adults

Background: Multi-morbidity in older adults is commonly associated with depressed mood. Similarly, subjective reports of pain are also associated with both physical illness and increased depressive symptoms. However, whether pain independently contributes to the experience of depression in older people with multi-morbidity has not been studied. Methods: In this study, participants were 1281 consecutive older adults presenting to one of 19 primary care services in Australia (recruitment rate = 75%). Participants were asked to indicate the presence of a number of common chronic illnesses, to rate their current pain severity and to complete the Geriatric Depression Scale. Results: Results confirmed that the number of medical illnesses reported was strongly associated with depressive symptoms. Twenty-six percent of participants with multi-morbidity scored in the clinical range for depressive symptoms in comparison to 15% of participants with no illnesses or a single illness. In regression analyses, the presence of chronic pain (t = 5.969, p < 0.0005), diabetes (t = 4.309, p < 0.0005), respiratory (t = 3.720, p < 0.0005) or neurological illness (t = 2.701, p = 0.007) were all independent contributors to depressive symptoms. Even when controlling for each individual illness, and the overall number of illnesses (t = 2.207, p = 0.028), pain severity remained an independent predictor of depressed mood (F change = 28.866, p < 0.0005, t = 5.373, p < 0.0005). Conclusions: Physicians should consider screening for mood problems amongst those with multi-morbidity, particularly those who experience pain

Problem-solving versus cognitive restructuring of medically ill seniors with depression (PROMISE-D trial): Study protocol and design

With an ageing population in most Western countries, people are living longer but often with one or more chronic physical health problems. Older people in physically poor health are at greater risk of developing clinical depression. Cognitive Behavioural Therapy (CBT) and Problem Solving Therapy (PST) have both been found to be efficacious in treating late-life depression, however patients with "multi-morbidity" (i.e. more than one chronic condition) are often excluded from these trials. The aim of this study is to compare the efficacy of CBT and PST in treating older adults who have one or more chronic physical health conditions and a diagnosable depressive disorder. This study will be the first to explicitly target the treatment of depression in older people in primary care settings presenting with a range of health problems using behavioural interventions. The PROMISE-D study is a randomised controlled trial of two evidence-based treatments for late-life major or minor depression for patients who also have at least one co-morbid chronic health problem. Participants will be randomised to two active interventions (PST or CBT) or enhanced treatment-as-usual (E-TAU). Primary outcomes will be depression diagnostic status and severity of depression (according to the Hamilton Depression Rating Scale and the Geriatric Depression Scale). Secondary outcomes will be anxiety severity, quality of life and health care utilisation. Assessments will be conducted by a researcher who remains blind to the patient's treatment allocation and will be conducted pre and post-treatment and at six and 12 months follow-up. Health care utilisation will be assessed throughout a two year period following entry to the trial. Executive function, rumination and emotion regulation will also be measured to determine the impact of these factors on treatment response in two treatment groups. Multi-morbidity, the experience of two or more chronic health problems, is becoming an increasing problem internationally, particularly amongst the elderly. Evidence-based psychological treatments exist for late-life depression and these have been shown to be effective for participants with individual health problems and depression. However, there are no studies that have compared the two leading psychotherapies shown to be effective in the treatment of late-life depression. In addition, many trials of psychotherapy with older adults exclude those with multi-morbidity. Hence, this trial will confirm whether CBT and PST are efficacious in the treatment of depression in the context of complex medical needs and determine which of these two interventions is most efficacious

Risksensitive online learning

Abstract. We consider the problem of online learning in settings in which we want to compete not simply with the rewards of the best expert or stock, but with the best trade-off between rewards and risk. Motivated by finance applications, we consider two common measures balancing returns and risk: the Sharpe ratio [11] and the mean-variance criterion of Markowitz [10]. We first provide negative results establishing the impossibility of no-regret algorithms under these measures, thus providing a stark contrast with the returns-only setting. We then show that the recent algorithm of Cesa-Bianchi et al. [5] achieves nontrivial performance under a modified bicriteria risk-return measure, and give a modified best expert algorithm that achieves no regret for a “localized” version of the mean-variance criterion. We perform experimental comparisons of traditional online algorithms and the new risk-sensitive algorithms on a recent six-year S&amp;P 500 data set. To our knowledge this paper initiates the investigation of explicit risk considerations in the standard models of worst-case online learning.