(searched for: 10.29328/journal.ijcv.1001029)
Published: 27 January 2021
International Journal of Clinical Virology, Volume 5, pp 001-010; https://doi.org/10.29328/journal.ijcv.1001029
Introduction: SARS-CoV-2 life cycle: The disease which reportedly began in Chinese city Wuhan in November-December 2019 manifesting as severe respiratory illness, soon spread to various parts of the world, and was named COVID-19, and declared a pandemic by WHO. The life cycle of SARS-CoV-2 begins with membrane fusion mediated by Spike (S) protein binding to the ACE2 receptors. Following viral entry and release of genome into the host cell cytoplasm there occurs replication and transcription to generate viral structural and non-structural proteins. Finally, VLPs are produced and the mature virions are released from the host cell. Immunogenicity of the spike protein: The S protein is considered the main antigenic component among structural proteins of SARS-CoV-2 and responsible for inducing the host immune response. The neutralising antibodies (nAbs) targeting the S protein are produced and may confer a protective immunity against the viral infection. Further, the role of the S protein in infectivity also makes it an important tool for diagnostic antigen-based testing and vaccine development. The S-specific antibodies, memory B and circulating TFH cells are consistently elicited following SARS-CoV-2 infection, and COVID-19 vaccine shots in clinical trials. The emerging SARS-CoV-2 variants: The early genomic variations in SARS-CoV-2 have gone almost unnoticed having lacked an impact on disease transmission or its clinical course. Some of the recently discovered mutations, however, have impact on transmissibility, infectivity, or immune response. One such mutation is the D614G variant, which has increased in prevalence to currently become the dominant variant world-over. Another, relatively new variant, named VUI-202012/01 or B.1.1.7 has acquired 17 genomic alterations and carries the risk of enhanced infectivity. Further, its potential impact on vaccine efficacy is a worrisome issue. Conclusion: THE UNMET CHALLENGES: COVID-19 as a disease and SARS-CoV-2 as its causative organism, continue to remain an enigma. While we continue to explore the agent factors, disease transmission dynamics, pathogenesis and clinical spectrum of the disease, and therapeutic modalities, the grievous nature of the disease has led to emergency authorizations for COVID-19 vaccines in various countries. Further, the virus may continue to persist and afflict for years to come, as future course of the disease is linked to certain unknown factors like effects of seasonality on virus transmission and unpredictable nature of immune response to the disease.
International Journal of Computer Vision, Volume 128, pp 2363-2365; https://doi.org/10.1007/s11263-020-01380-5
The publisher has not yet granted permission to display this abstract.
Published: 20 July 2017
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV.
Published: 9 June 2014
Manifold Learning for 3D Shape Description and Classification; https://doi.org/10.21236/ada606874
Periodically, the US Army conducts detailed measurement surveys of its soldiers as a way to understand the impact that changes in soldier body size have for the design, fit and sizing of virtually every piece of clothing and equipment that Soldiers wear and use in combat. Recently finished US Army Anthropometric Survey (ANSUR II) has collected 3D body scan data of soldiers at the Natick Solider Center (NSC), as shown in Figure 1. By applying new techniques for shape analysis and classification to these 3D body scan data will help designers of clothing and personal protection equipment to understand and fit Army population. The overall research goal of this proposal is to create a new manifold learning framework for large-scale graph decomposition and approximation problems by low-rank approximation and guarantee computable, stable and fast optimizations for 3D shape description and classification. The PI's group has published (or accepted for publication) 1 book through Springer and 13 scientific papers partially supported by this grant. In particular, these papers are in top journals and conference proceedings such as TPAMI, IJCV, TCSVT, ICCV, AAAI, SDM, ACM MM, etc. One paper, 1 out of 384, receives the Best Paper Award in SDM 2014. The PI, Dr. Y. Raymond Fu has received the 2014 INNS Young Investigator Award, from International Neural Networks Society (INNS), 2014. Leveraged by this grant, the PI has been granted an ARO Young Investigator Program (YIP) Award and a Defense University Research Instrumentation Program (DURIP) award.