The growth and evolution of thin oxide films on δ-plutonium surfaces

The common oxides of plutonium are the dioxide (PuO2) and the sesquioxide (Pu2O3). The nature of an oxide on plutonium metal under air at room temperature is typically described as a thick PuO2 film at the gas-oxide interface with a thinner Pu2O3 film near the oxide-metal substrate interface. In a reducing environment, such as ultra high vacuum, the dioxide (Pu4+; O/Pu=2.0) readily converts to the sesquioxide (Pu3+; O/Pu=1.5) with time. In this work, the growth and evolution of thin plutonium oxide films is studied with x-ray photoelectron spectroscopy (XPS) under varying conditions. The results indicate that, like the dioxide, the sesquioxide is not stable on a clean metal substrate under reducing conditions, resulting in substoichiometric films (Pu2O3-y). The Pu2O3-y films prepared exhibit a variety of stoichiometries (y~0.2-1) as a function of preparation conditions, highlighting the fact that caution must be exercised when studying plutonium oxide surfaces under these conditions and interpreting resulting data

The growth and evolution of thin oxide films on δ-plutonium surfaces

The common oxides of plutonium are the dioxide (PuO2) and the sesquioxide (Pu2O3). The nature of an oxide on plutonium metal under air at room temperature is typically described as a thick PuO2 film at the gas-oxide interface with a thinner Pu2O3 film near the oxide-metal substrate interface. In a reducing environment, such as ultra high vacuum, the dioxide (Pu4+; O/Pu=2.0) readily converts to the sesquioxide (Pu3+; O/Pu=1.5) with time. In this work, the growth and evolution of thin plutonium oxide films is studied with x-ray photoelectron spectroscopy (XPS) under varying conditions. The results indicate that, like the dioxide, the sesquioxide is not stable on a clean metal substrate under reducing conditions, resulting in substoichiometric films (Pu2O3-y). The Pu2O3-y films prepared exhibit a variety of stoichiometries (y~0.2-1) as a function of preparation conditions, highlighting the fact that caution must be exercised when studying plutonium oxide surfaces under these conditions and interpreting resulting data

Streamline Integration using MPI-Hybrid Parallelism on a Large Multi-Core Architecture

Streamline computation in a very large vector field data set represents a significant challenge due to the non-local and datadependentnature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programmingand execution as applied to streamline integration on a large, multicore platform. With multi-core processors now prevalent in clustersand supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice.We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize-over-seeds and parallelize-overblocks,and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing betweencores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication andI/O bandwidth than a traditional, non-hybrid distributed implementation

MGARD+: Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction

Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which treats the multilevel decomposition as a preconditioner and terminates the decomposition process at an appropriate level. 3) We leverage a set of algorithmic optimization strategies to significantly improve the performance of multilevel decomposition/recompositing. 4) We evaluate our proposed method using four real-world scientific datasets and compare with several state-of-the-art lossy compressors. Experiments demonstrate that our optimizations improve the decomposition/recompositing performance of the existing multilevel method by up to $70 \times$70x, and the proposed compression method can improve compression ratio by up to $2 \times$2x compared with other state-of-the-art error-bounded lossy compressors under the same level of data distortion

Occam's Razor and Petascale Visual Data Analysis

One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today?s largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena

Symptom-based stratification of patients with primary Sjögren's syndrome: multi-dimensional characterisation of international observational cohorts and reanalyses of randomised clinical trials

Background Heterogeneity is a major obstacle to developing effective treatments for patients with primary Sjögren's syndrome. We aimed to develop a robust method for stratification, exploiting heterogeneity in patient-reported symptoms, and to relate these differences to pathobiology and therapeutic response. Methods We did hierarchical cluster analysis using five common symptoms associated with primary Sjögren's syndrome (pain, fatigue, dryness, anxiety, and depression), followed by multinomial logistic regression to identify subgroups in the UK Primary Sjögren's Syndrome Registry (UKPSSR). We assessed clinical and biological differences between these subgroups, including transcriptional differences in peripheral blood. Patients from two independent validation cohorts in Norway and France were used to confirm patient stratification. Data from two phase 3 clinical trials were similarly stratified to assess the differences between subgroups in treatment response to hydroxychloroquine and rituximab. Findings In the UKPSSR cohort (n=608), we identified four subgroups: Low symptom burden (LSB), high symptom burden (HSB), dryness dominant with fatigue (DDF), and pain dominant with fatigue (PDF). Significant differences in peripheral blood lymphocyte counts, anti-SSA and anti-SSB antibody positivity, as well as serum IgG, κ-free light chain, β2-microglobulin, and CXCL13 concentrations were observed between these subgroups, along with differentially expressed transcriptomic modules in peripheral blood. Similar findings were observed in the independent validation cohorts (n=396). Reanalysis of trial data stratifying patients into these subgroups suggested a treatment effect with hydroxychloroquine in the HSB subgroup and with rituximab in the DDF subgroup compared with placebo. Interpretation Stratification on the basis of patient-reported symptoms of patients with primary Sjögren's syndrome revealed distinct pathobiological endotypes with distinct responses to immunomodulatory treatments. Our data have important implications for clinical management, trial design, and therapeutic development. Similar stratification approaches might be useful for patients with other chronic immune-mediated diseases. Funding UK Medical Research Council, British Sjogren's Syndrome Association, French Ministry of Health, Arthritis Research UK, Foundation for Research in Rheumatology

Determinants of recovery from post-COVID-19 dyspnoea: analysis of UK prospective cohorts of hospitalised COVID-19 patients and community-based controls

Background The risk factors for recovery from COVID-19 dyspnoea are poorly understood. We investigated determinants of recovery from dyspnoea in adults with COVID-19 and compared these to determinants of recovery from non-COVID-19 dyspnoea. Methods We used data from two prospective cohort studies: PHOSP-COVID (patients hospitalised between March 2020 and April 2021 with COVID-19) and COVIDENCE UK (community cohort studied over the same time period). PHOSP-COVID data were collected during hospitalisation and at 5-month and 1-year follow-up visits. COVIDENCE UK data were obtained through baseline and monthly online questionnaires. Dyspnoea was measured in both cohorts with the Medical Research Council Dyspnoea Scale. We used multivariable logistic regression to identify determinants associated with a reduction in dyspnoea between 5-month and 1-year follow-up. Findings We included 990 PHOSP-COVID and 3309 COVIDENCE UK participants. We observed higher odds of improvement between 5-month and 1-year follow-up among PHOSP-COVID participants who were younger (odds ratio 1.02 per year, 95% CI 1.01–1.03), male (1.54, 1.16–2.04), neither obese nor severely obese (1.82, 1.06–3.13 and 4.19, 2.14–8.19, respectively), had no pre-existing anxiety or depression (1.56, 1.09–2.22) or cardiovascular disease (1.33, 1.00–1.79), and shorter hospital admission (1.01 per day, 1.00–1.02). Similar associations were found in those recovering from non-COVID-19 dyspnoea, excluding age (and length of hospital admission). Interpretation Factors associated with dyspnoea recovery at 1-year post-discharge among patients hospitalised with COVID-19 were similar to those among community controls without COVID-19. Funding PHOSP-COVID is supported by a grant from the MRC-UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research (NIHR) rapid response panel to tackle COVID-19. The views expressed in the publication are those of the author(s) and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health and Social Care. COVIDENCE UK is supported by the UK Research and Innovation, the National Institute for Health Research, and Barts Charity. The views expressed are those of the authors and not necessarily those of the funders