Evaluation of Commercially Available High-Throughput SARS-CoV-2 Serologic Assays for Serosurveillance and Related Applications.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serosurveys can estimate cumulative incidence for monitoring epidemics, requiring assessment of serologic assays to inform testing algorithm development and interpretation of results. We conducted a multilaboratory evaluation of 21 commercial high-throughput SARS-CoV-2 serologic assays using blinded panels of 1,000 highly characterized specimens. Assays demonstrated a range of sensitivities (96%-63%), specificities (99%-96%), and precision (intraclass correlation coefficient 0.

Use of US Blood Donors for National Serosurveillance of Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies: Basis for an Expanded National Donor Serosurveillance Program.

Background: The Recipient Epidemiology and Donor Evaluation Study-IV-Pediatric (REDS-IV-P) Epidemiology, Surveillance and Preparedness of the Novel SARS-CoV-2 Epidemic (RESPONSE) seroprevalence study conducted monthly cross-sectional testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in blood donors in 6 US metropolitan regions to estimate the extent of SARS-CoV-2 infections over time.

Methods: During March-August 2020, approximately ≥1000 serum specimens were collected monthly from each region and tested for SARS-CoV-2 antibodies using a well-validated algorithm. Regional seroprevalence estimates were weighted based on demographic differences compared with the general population.

Multicenter evaluation of the performance characteristics of the bayer VERSANT HCV RNA 3.0 assay (bDNA).

In this multicenter evaluation, the VERSANT HCV RNA 3.0 Assay (bDNA) (Bayer Diagnostics, Tarrytown, N.Y.

A dynamic recurrent neural network for multiple muscles electromyographic mapping to elevation angles of the lower limb in human locomotion.

This paper describes the use of a dynamic recurrent neural network (DRNN) for simulating lower limb coordination in human locomotion. The method is based on mapping between the electromyographic signals (EMG) from six muscles and the elevation angles of the three main lower limb segments (thigh, shank and foot). The DRNN is a fully connected network of 35 hidden units taking into account the temporal relationships history between EMG and lower limb kinematics.