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1.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333608

ABSTRACT

SARS-CoV-2 seroprevalence was low (<1%) in this large population of healthcare workers (HCWs) across the state of Tennessee (n=11,787) in May-June 2020. Among those with PCR results, 81.5% of PCR and antibody test results were concordant. SARS-CoV-2 seroprevalence was higher among HCWs working in high-community-transmission regions and among younger workers. IMPORTANCE: These results may be seen as a baseline assessment of SARS-CoV-2 seroprevalence among HCWs in the American South during a period of growth, but not yet saturation, of infections among susceptible populations. In fact, this period of May-June 2020 was marked by the extension of renewed and sustained community-wide transmission after mandatory quarantine periods expired in several more populous regions of Tennessee. Where community transmission remains low, HCWs may still be able to effectively mitigate SARS-CoV-2 transmission, preserving resources for populations at high risk of severe disease, and these sorts of data help highlight such strategies.

2.
Current Medical Research and Opinion ; 37(SUPPL 1):38, 2021.
Article in English | EMBASE | ID: covidwho-1254183

ABSTRACT

Objective: Use of preprint servers by academics has accelerated during the SARS-CoV-2 pandemic, but whether their use by industry has increased over this period is unclear. We compared the use of medRxiv for the dissemination of COVID versus non- COVID research, focussing on industry-sponsored studies. Research design and methods: Using the search function in medRxiv (https://www.medrxiv.org/), we determined the total numbers of preprints mentioning 'COVID-19 or SARS-CoV-2' (COVID) and all others (non-COVID) deposited between 1 January and 30 September 2020. We then manually screened all COVID and non-COVID preprints deposited during a peak-activity week (24-31 May) to determine subject area, study type, author affiliations and funding source. Results: As of 30 September, 7449 COVID and 3335 non-COVID preprints had been deposited on medRxiv, including 404 and 129, respectively, over 24-31 May. The top two subject areas represented by COVID preprints were 'Infectious disease' (35.1%) and 'Epidemiology' (27.7%). Non-COVID preprints were deposited under a range of subject areas, most frequently 'Epidemiology' (10.9%) and 'Neurology' (8.5%). The highest proportion of preprints (COVID, 32.7%;non-COVID, 38.0%) described observational research (32.4%) and modelling studies (12.4%). Ten (2.5%) COVID preprints acknowledged commercial funding, versus 6 (4.7%) non-COVID. Industry authors were listed on 9 (90%) and 5 (83%) of these, respectively. While half of industry-sponsored non-COVID preprints described observational research, those on COVID covered a range of study types, including artificial intelligence, diagnostics, modelling, and observational research. Conclusions: Although the use of medRxiv has accelerated during the pandemic, utilization by industry has remained low. Further research is needed to identify potential barriers to the industry embracing preprint servers.

3.
Current Medical Research and Opinion ; 37:38-38, 2021.
Article in English | Web of Science | ID: covidwho-1226123
4.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-8276

ABSTRACT

We present the extension of the Tinker-HP package (Lagard\`ere et al., Chem. Sci., 2018,9, 956-972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamics simulations using polarizable many-body force fields. The new high-performance module allows for an efficient use of single- and multi-GPU architectures ranging from research laboratories to modern pre-exascale supercomputer centers. After detailing an analysis of our general scalable strategy that relies on OpenACC and CUDA, we discuss the various capabilities of the package. Among them, the multi-precision possibilities of the code are discussed. If an efficient double precision implementation is provided to preserve the possibility of fast reference computations, we show that a lower precision arithmetic is preferred providing a similar accuracy for molecular dynamics while exhibiting superior performances. As Tinker-HP is mainly dedicated to accelerate simulations using new generation point dipole polarizable force field, we focus our study on the implementation of the AMOEBA model and provide illustrative benchmarks of the code for single- and multi-cards simulations on large biosystems encompassing up to millions of atoms. The new code strongly reduces time to solution and offers the best performances ever obtained using the AMOEBA polarizable force field. Perspectives toward the strong-scaling performance of our multi-node massive parallelization strategy, unsupervised adaptive sampling and large scale applicability of the Tinker-HP code in biophysics are discussed. The present software has been released in phase advance on GitHub in link with the High Performance Computing community COVID-19 research efforts and is free for Academics (see https://github.com/TinkerTools/tinker-hp).

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