Object-Based Caching for MPI-IO

As the size of the data sets manipulated by data-intensive scientific applications approaches the petabyte level and beyond, the need for scalable I/O techniques becomes increasingly important and difficult. Much of the research on this issue has been performed within the context ofMPI-IO: the de-facto standard parallel I/O interface for data-intensive applications. Its popularity stems from the fact that MPI-IO provides to applications a rich and flexile parallel I/O API coupled with highly efficient implementations of this API. This problem is being further addressed by the development of powerful parallel I/O subsystems, and state-of-the-art file systems that can efficiently access this infrastructure. However, even with such advances, I/O continues to be a significant bottleneck in application performance.The goal of this research is to provide high-performance I/O for data-intensive applications. A key insight is that a major obstacle in the way of this goal is the legacy view of a file as a linear sequence of bytes. This is because scientific applications rarely access data in a way that matches this file model, using instead what is more accurately described as an object model. In fact, it is the runtime translation between these two data models that is a major contributor to poor I/O performance. To address this issue, this research will develop a more powerful object-based file model for MPI applications, and an object-based caching system to serve as an interface between MPI applications and object-based files. Objects will be carefully defined to encapsulate information about an application\u27s I/O access patterns, and such information will be used to increase the parallelism of file accesses and decrease the cost of maintaining global cache coherence

MRI: Acquisition of a High Performance Cluster for the University of Maine Scientific Grid Portal

This project, acquiring a cluster to establish a scientific grid portal in Maine, aims to enable projects requiring large datasets. The work makes available to the wider community results such as widely-used whole-ice sheet models, tools for climate change research, prototype versions of object-based caching system (bundled with MPI-IO implementation developed at Argonne National Lab), the data management system, real-time animations, videos, etc. Additionally, the portal provides the larger community the compute power, storage capacity, and rendering engine to execute very high-resolution models, and receive animations and other visualized information in real time.Broader Impact: The infrastructure enhances understanding of global issues and contributes in the development of educational tools for K-12 students. The scientific grid portal contributes in the dissemination of important scientific discoveries. The portal also provides a show-case for research being performed in the state

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Cardiovascular Health in Anxiety or Mood Problems Study (CHAMPS): study protocol for a randomized controlled trial

Background: Previous psychological and pharmacological interventions have primarily focused on depression disorders in populations with cardiovascular diseases (CVDs) and the efficacy of anxiety disorder interventions is only more recently being explored. Transdiagnostic interventions address common emotional processes and the full range of anxiety and depression disorders often observed in populations with CVDs. The aim of CHAMPS is to evaluate the feasibility of a unified protocol (UP) for the transdiagnostic treatment of emotional disorders intervention in patients recently hospitalized for CVDs. The current study reports the protocol of a feasibility randomized controlled trial to inform a future trial. Methods/Design: This is a feasibility randomized, controlled trial with a single-center design. A total of 50 participants will be block-randomized to either a UP intervention or enhanced usual care. Both groups will receive standard CVD care. The UP intervention consists of 1) enhancing motivation, readiness for change, and treatment engagement; (2) psychoeducation about emotions; (3) increasing present focused emotion awareness; (4) increasing cognitive flexibility; (5) identifying and preventing patterns of emotion avoidance and maladaptive emotion-driven behaviors (EDBs, including tobacco smoking, and alcohol use); (6) increasing tolerance of emotion-related physical sensations; (7) interoceptive and situation-based emotion-focused exposure; and (8) relapse prevention strategies. Treatment duration is 12 to 18 weeks. Relevant outcomes include the standard deviation of self-rated anxiety, depression and quality of life symptoms. Other outcomes include intervention acceptability, satisfaction with care, rates of EDBs, patient adherence, physical activity, cardiac and psychiatric readmissions. Parallel to the main trial, a nonrandomized comparator cohort will be recruited comprising 150 persons scoring below the predetermined depression and anxiety severity thresholds. Discussion: CHAMPS is designed to evaluate the UP for the transdiagnostic treatment of emotional disorders targeting emotional disorder processes in a CVD population. The design will provide preliminary evidence of feasibility, attrition, and satisfaction with treatment to design a definitive trial. If the trial is feasible, it opens up the possibility for interventions to target broader emotional processes in the precarious population with CVD and emotional distress.Phillip J. Tully, Deborah A. Turnbull, John D. Horowitz, John F. Beltrame, Terina Selkow, Bernhard T. Baune, Elizabeth Markwick, Shannon Sauer-Zavala, Harald Baumeister, Suzanne Cosh and Gary A. Witter

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Classifiers for the Causes of Data Loss: An Important Step Towards Intelligent, Adaptive, and Efficient Communication Services Abstract

The goal of this research is to develop a set of intelligent, adaptive, and efficient communication services for Grid computing. An important milestone on the path to such next-generation communication systems is the development of a classification mechanism that can distinguish between the various causes of data loss in cluster/Grid environments. In this paper, we discuss our approach to developing such a system