OBJECTIVE: To describe maternal and umbilical cord blood anti-spike immunoglobulin (Ig)G levels at delivery with coronavirus disease 2019 (COVID-19) vaccination before and during pregnancy and to assess the association of prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and a vaccine booster dose with anti-spike maternal and umbilical cord IgG levels. METHODS: We conducted a retrospective cohort study of women with self-reported COVID-19 vaccination (Pfizer-BioNTech, Moderna, or Johnson & Johnson/Janssen), including a booster dose, during or before pregnancy, who delivered at 34 weeks of gestation or more. Maternal and umbilical cord blood samples at delivery were analyzed for semi-quantitative anti-spike IgG. We examined the association between timing of maternal vaccination and maternal and umbilical cord anti-spike levels using a rank sum test. The relationships between a prior history of SARS-CoV-2 infection and maternal and umbilical cord anti-spike IgG levels, and between a booster dose and maternal and umbilical cord anti-spike levels, were also evaluated using a rank sum test. RESULTS: We included data from 1,359 vaccinated pregnant women, including 20 women who received a booster dose, and 1,362 umbilical cord samples. Maternal anti-spike IgG levels were detectable at delivery regardless of timing of vaccination throughout pregnancy among fully vaccinated women; however, early third-trimester vaccination was associated with the highest anti-spike IgG levels in maternal and umbilical cord blood. Among women with a history of SARS-CoV-2 infection, maternal and cord blood antibody response achieved with vaccination in early pregnancy was comparable with third-trimester vaccination in pregnant women without a history of SARS-CoV-2 infection. A booster dose in the third trimester was associated with maternal anti-spike IgG levels greater than third-trimester vaccination in women with or without a history of SARS-CoV-2 infection. DISCUSSION: Vaccination against COVID-19 before and throughout pregnancy was associated with detectable maternal anti-spike IgG levels at delivery. A complete vaccination course, prior history of SARS-CoV-2 infection, and a third-trimester booster dose were associated with the highest maternal and umbilical cord antibody levels.

The chr12q24.13 locus encoding OAS1–OAS3 antiviral proteins has been associated with coronavirus disease 2019 (COVID-19) susceptibility. Here, we report genetic, functional and clinical insights into this locus in relation to COVID-19 severity. In our analysis of patients of European (n = 2,249) and African (n = 835) ancestries with hospitalized versus nonhospitalized COVID-19, the risk of hospitalized disease was associated with a common OAS1 haplotype, which was also associated with reduced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clearance in a clinical trial with pegIFN-λ1. Bioinformatic analyses and in vitro studies reveal the functional contribution of two associated OAS1 exonic variants comprising the risk haplotype. Derived human-specific alleles rs10774671-A and rs1131454-A decrease OAS1 protein abundance through allele-specific regulation of splicing and nonsense-mediated decay (NMD). We conclude that decreased OAS1 expression due to a common haplotype contributes to COVID-19 severity. Our results provide insight into molecular mechanisms through which early treatment with interferons could accelerate SARS-CoV-2 clearance and mitigate against severe COVID-19.

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Night driving is one of the major factors which affects traffic safety. Although detecting oncoming vehicles at night time is a challenging task, it may improve traffic safety. If the oncoming vehicle is recognised in good time, this will motivate drivers to keep their eyes on the road. The purpose of this paper is to present an approach to detect vehicles at night based on the employment of a single onboard camera. This system is based on detecting vehicle headlights by recognising their shapes via an SVM classifier which was trained for this purpose. A pairing algorithm was designed to pair vehicle headlights to ensure that the two headlights belong to the same vehicle. A multi-object tracking algorithm was invoked to track the vehicle throughout the time the vehicle is in the scene. The system was trained with 503 single objects and tested using 144 587 single objects which were extracted from 1410 frames collected from 15 videos and 27 moving vehicles. It was found that the accuracy of recognition was 97.9% and the vehicle recognition rate was 96.3% which indicates clearly the high robustness attained by this system.