ABSTRACT
Background: IgG anti-spike (S) antibodies arise after SARS-CoV-2 infection aswellas vaccination. LevelsofIgG anti-S are linked to neutralizing antibody titers and protection against (re)infection. Method(s): We measured IgG anti-S and surrogate neutralizing antibody kinetics against Wild Type (WT) and 4 Variants of Concern (VOC) in health care workers (HCW) 3 and 10 months after natural infection ("infection", n=83) or vaccination (2 doses of BNT162b2) with ("hybrid immunity", n=17) or without prior SARS-CoV-2 infection ("vaccination", n=97). Result(s): The humoral immune response in the "vaccination" cohort was higher at 3 months, but lower at 10 months, compared to the "infection" cohort due to a faster decline. The "hybrid immunity" cohort had the highest antibody levels at 3 and 10 months with a slower decline compared to the "vaccination" cohort (figure 1). Surrogate neutralizing antibody levels (expressed as %inhibition of ACE-2 binding) showed a linear relation with log10 of IgG anti-S against WT and four VOC. IgG anti-S corresponding to 90% inhibition ranged from 489 BAU/mL for WT to 1756 BAU/mL for Beta variant. Broad pseudoneutralization predicted live virus neutralization of Omicron BA.1 in 20 randomly selected high titer samples. Conclusion(s): Hybrid immunity resulted in the strongest humoral immune response. Antibodies induced by natural infection decreased more slowly than after vaccination, resulting in higher antibody levels at 10 months compared to vaccinated HCW without prior infection. There was a linear relationship between surrogate neutralizing activity and log10 IgG anti-S for WT and 4 VOC, although some VOC showed reduced sensitivity to pseudoneutralization.
ABSTRACT
Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth ( ∼ 100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos ('talking-head videos') to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users ( n=242 ) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git. © 1983-2012 IEEE.
ABSTRACT
Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth (~100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos (“talking-head videos”) to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users (n = 242) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git. IEEE
ABSTRACT
The home is a place of shelter, a place for family, and for separation from other parts of life, such as work. Global challenges, the most pressing of which are currently the COVID-19 pandemic and climate change has forced extra roles into many homes and will continue to do so in the future. Biodesign integrates living organisms into designed solutions and can offer opportunities for new kinds of technologies to facilitate a transition to the home of the future. Many families have had to learn to work alongside each other, and technology has mediated a transition from standard models of operation for industries. These are the challenges of the 21st century that mandate careful thinking around interactive systems and innovations that support new ways of living and working at home. In this workshop, we will explore opportunities for biodesign interactive systems in the future home. We will bring together a broad group of researchers in HCI, design, and biosciences to build the biodesign community and discuss speculative design futures. The outcome will generate an understanding of the role of interactive biodesign systems at home, as a place with extended functionalities.
ABSTRACT
During the pandemic, wearables such as face masks and face shields have become broadly adopted, these solutions do reduce infection but do not eliminate infectious agents from surfaces and objects the person may touch. Therefore, regular disinfection of hands and frequently touched surfaces is a critical factor in preventing the spread of infectious diseases ranging from the common cold and flu to SARS and COVID-19. This activity of frequent disinfection requires a high degree of discipline and leads to increased cognitive and physical effort involved in frequent washing of hands or use of a pocket sanitizer. We present an open-source, wearable sanitizer that provides just-in-time, automatic dispensing of alcohol to the wearer's hand or nearby objects using sensors and programmable cues. We systematically explore the design space aiming to create a device that not only seamlessly integrates with the user's body and behavior but also frees their physical and mental faculties for other tasks. © 2020 Owner/Author.