Objectives Serologic detection of prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is needed for definition of convalescent plasma donors, for confounding SARS-CoV-2 presentation, and for seroprevalence studies. Reliable serologic assays with independent validation are required. Methods Six SARS-CoV-2 antibody assays from Beckman Coulter, Euroimmun (IgG, IgA), Roche, and Siemens (Centaur, Vista) were assessed for specificity (n = 184), sensitivity (n = 154), and seroconversion in a defined cohort with clinical correlates and molecular SARS-CoV-2 results. Results Assay specificity was 99% or greater for all assays except the Euroimmun IgA (95%). Sensitivity at more than 21 days from symptom onset was 84%, 95%, 72%, 98%, 67%, and 96% for Beckman Coulter, Centaur, Vista, Roche, Euroimmun IgA, and Euroimmun IgG, respectively. Average day of seroconversion was similar between assays (8-10 d), with 2 patients not producing nucleocapsid antibodies during hospitalization. Conclusions SARS-CoV-2 nucleocapsid antibodies may be less reliably produced early in disease than spike protein antibodies. Assessment of convalescent plasma donors at more than 30 days from symptom onset and seroprevalence studies should use assays with defined sensitivity at time points of interest because not all assays detected antibodies reliably at more than 30 days.
Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.
Of the many features that smart cities offer, safe and comfortable mobility of pedestrians within the built environment is of particular importance. Safe and comfortable mobility requires that the built environments of smart cities be accessible to all pedestrians, mobility abled and mobility impaired, given their various mobility needs and preferences. This, coupled with advanced technologies such as wayfinding applications, pedestrians can get assistance in finding the best pathways at different locations and times. Wayfinding applications comprise two components, a database component containing accessibility data, and appropriate algorithms that can utilize accessibility data to meet the mobility needs and preferences of all individuals. While wayfinding applications that provide accessibility on both permanent (e.g., steps) and temporary (e.g., snow) pathways are becoming available, there is a gap in current solutions. There are two elements in the gap, one is that the accessibility data used for finding accessible pathways for people with disabilities are not compliant to the widely agreed upon and available standards, another is that the accessibility data are not available in free and open platforms so that they can be used by developers to develop personalized wayfinding applications and services. To fill this gap, in this paper, we propose a new extension in CityGML with accessibility data. We demonstrate the benefits of the new extension by testing various route options within a city. These route options clearly show the differences between commonly (shortest and fastest) requested and produced pathways and accessible pathways that are feasible and preferred by people who are mobility impaired, such as wheelchair users.
Objectives Acute viral infections and some vaccines have been shown to increase false positivity in serologic assays. We assessed if the messenger RNA coronavirus disease 2019 (COVID-19) vaccines could cause false reactivity in common serologic assays in a pilot longitudinal cohort. Methods Thirty-eight participants with sera available prevaccination, 2 weeks after each vaccine dose, and monthly thereafter for up to 5 months were tested for common infectious disease serologies and antiphospholipid syndrome (APS) serology markers on the BioPlex 2200, Sure-Vue rapid plasma reagin (RPR), and Macro-Vue RPR. Twenty-two participants received the Moderna vaccine and 16 received the Pfizer vaccine. Results Most assays had no change in reactivity over the course of the sample draws, including APS markers. Epstein-Barr virus immunoglobulin G (IgG), measles IgG, and rubella immunoglobulin M all had possible false reactivity in one to two participants. RPR tests demonstrated false reactivity, with baseline nonreactive participant samples becoming reactive following vaccination. There were more false reactive participants (7/38) in the BioPlex RPR than in the Sure-Vue (2/38) and Macro-Vue (1/38) tests. All falsely reactive RPR tests were in participants who received the Moderna vaccine. Conclusions Serologic assays with results that do not fit the clinical picture following COVID-19 vaccination should be repeated. Effects of false reactivity can last more than 5 months in some assays. In particular, RPR is susceptible to false reactivity, and there is variability among assays. Larger longitudinal studies are needed to determine the incidence and window of false reactivity.
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