Background In this study, we aimed to evaluate the effects of tocilizumab in adult patients admitted to hospital with COVID-19 with both hypoxia and systemic inflammation. Methods This randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing several possible treatments in patients hospitalised with COVID-19 in the UK. Those trial participants with hypoxia (oxygen saturation <92% on air or requiring oxygen therapy) and evidence of systemic inflammation (C-reactive protein ≥75 mg/L) were eligible for random assignment in a 1:1 ratio to usual standard of care alone versus usual standard of care plus tocilizumab at a dose of 400 mg–800 mg (depending on weight) given intravenously. A second dose could be given 12–24 h later if the patient's condition had not improved. The primary outcome was 28-day mortality, assessed in the intention-to-treat population. The trial is registered with ISRCTN (50189673) and ClinicalTrials.gov ( NCT04381936 ). Findings Between April 23, 2020, and Jan 24, 2021, 4116 adults of 21 550 patients enrolled into the RECOVERY trial were included in the assessment of tocilizumab, including 3385 (82%) patients receiving systemic corticosteroids. Overall, 621 (31%) of the 2022 patients allocated tocilizumab and 729 (35%) of the 2094 patients allocated to usual care died within 28 days (rate ratio 0·85; 95% CI 0·76–0·94; p=0·0028). Consistent results were seen in all prespecified subgroups of patients, including those receiving systemic corticosteroids. Patients allocated to tocilizumab were more likely to be discharged from hospital within 28 days (57% vs 50%; rate ratio 1·22; 1·12–1·33; p<0·0001). Among those not receiving invasive mechanical ventilation at baseline, patients allocated tocilizumab were less likely to reach the composite endpoint of invasive mechanical ventilation or death (35% vs 42%; risk ratio 0·84; 95% CI 0·77–0·92; p<0·0001). Interpretation In hospitalised COVID-19 patients with hypoxia and systemic inflammation, tocilizumab improved survival and other clinical outcomes. These benefits were seen regardless of the amount of respiratory support and were additional to the benefits of systemic corticosteroids. Funding UK Research and Innovation (Medical Research Council) and National Institute of Health Research.

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Using quantum devices supported by classical computational resources is a promising approach to quantum-enabled computation. One example of such a hybrid quantum-classical approach is the variational quantum eigensolver (VQE) built to utilize quantum resources for the solution of eigenvalue problems and optimizations with minimal coherence time requirements by leveraging classical computational resources. These algorithms have been placed among the candidates for first to achieve supremacy over classical computation. Here, we provide evidence for the conjecture that variational approaches can automatically suppress even non-systematic decoherence errors by introducing an exactly solvable channel model of variational state preparation. Moreover, we show how variational quantum-classical approaches fit in a more general hierarchy of measurement and classical computation that allows one to obtain increasingly accurate solutions with additional classical resources. We demonstrate numerically on a sample electronic system that this method both allows for the accurate determination of excited electronic states as well as reduces the impact of decoherence, without using any additional quantum coherence time or formal error correction codes.First conceived of by Richard Feynman [1], quantum computers have the potential to offer radical advances in solving important problems ranging from optimization and eigenvalue problems to materials design. One problem of particular recent interest is that of quantum chemistry, where quantum computers have the potential to offer an exponential speedup in the determination of physical and chemical properties [2][3][4]. This problem has received attention both because of its great practical utility, and because it is believed that it may be one of the first approaches to demonstrate the superiority of a quantum computer over currently available classical computers [5,6].Recently, there have been a number of advances in quantum chemistry on quantum computers both algorithmically and technologically. The original work utilized the quantum phase estimation algorithm [7-9] and analyzed the use of adiabatic state preparation in chemical problems. Since then, the cost of the quantum phase estimation procedure has been brought down dramatically through considerations of physical locality of interactions, chemical insights, and more general algorithmic enhancements [10][11][12][13][14]. Additionally, prototype implementations of many of these algorithms have now been verified in the lab on quantum technologies such as quantum photonics, ion traps, NMR computers, and nitrogen vacancies in diamond [15][16][17][18][19][20].While there have been significant developments in quantum hardware across a variety of platforms, many of these algorithms cannot be faithfully run on current or near-future technology. To combat this problem, a hybrid quantum classical approach was developed, with the the idea that quantum processors should only be * Corresponding author: jmcclean@lbl.gov FIG. 1. A cartoon sche...

Background: Steroid-responsive encephalopathy associated with autoimmune thyroiditis (SREAT), often termed Hashimoto encephalopathy, is a poorly understood and often misdiagnosed entity. Objective: To characterize the clinical, laboratory, and radiologic findings in patients with SREAT to potentially improve recognition of this treatable entity. Design: Retrospective analysis of clinical features and diagnostic test data. Setting: Two affiliated tertiary care referral institutions. Patients: Twenty consecutive (6 male) patients diagnosed as having SREAT from 1995 to 2003. Main Outcome Measures: Clinical features and ancillary test findings associated with SREAT. Results: The median age at disease onset was 56 years (range, 27-84 years). The most frequent clinical features were tremor in 16 (80%), transient aphasia in 16 (80%), myoclonus in 13 (65%), gait ataxia in 13 (65%), seizures in 12 (60%), and sleep abnormalities in 11 (55%). All patients were assigned an alternative misdiagnosis at

Understanding the most efficient design and utilization of emerging multicore systems is one of the most challenging questions faced by the mainstream and scientific computing industries in several decades. Our work explores multicore stencil (nearest-neighbor) computations-a class of algorithms at the heart of many structured grid codes, including PDE solvers. We develop a number of effective optimization strategies, and build an auto-tuning environment that searches over our optimizations and their parameters to minimize runtime, while maximizing performance portability. To evaluate the effectiveness of these strategies we explore the broadest set of multicore architectures in the current HPC literature, including the Intel Clovertown, AMD Barcelona, Sun Victoria Falls, IBM QS22 PowerXCell 8i, and NVIDIA GTX280. Overall, our auto-tuning optimization methodology results in the fastest multicore stencil performance to date. Finally, we present several key insights into the architectural tradeoffs of emerging multicore designs and their implications on scientific algorithm development. *. Node power under a computational load can differ dramatically from both idle power and from the manufacturer's peak power specifications.