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(searched for: 10.29328/journal.ijcv.1001041)
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Mbow Moustapha, Diallo Ibrahima, Diouf Mamadou, Cissé Marouba, Gningue Moctar, Mboup Aminata, Leye Nafissatou, Lo Gora, Dia Yacine Amet, Padane Abdou, et al.
International Journal of Clinical Virology, Volume 6, pp 001-006; https://doi.org/10.29328/journal.ijcv.1001041

Abstract:
Purpose: Real-time reverse-transcription polymerase chain reaction (RT-PCR)-based testing remains the gold standard for the diagnosis of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the high diagnosis demand of SARS-CoV-2 and the limited resources for RT-PCR testing, especially in Low-Income Countries (LICs), antigen-based methods are being considered as an option. The aim of this study was to assess the performance of LumiraDx SARS-CoV-2 antigen assay for large population screening compared to RT-PCR. Methods: This evaluation was conducted on 4146 participants including travelers and participants under household survey and vaccine evaluation studies before injection of the first dose. Oropharyngeal and nasopharyngeal swaps were collected from each participant into 2 mL of viral transport medium (VTM) and 400 μl of VTM were used to assess the performance of LumiraDx SARS-CoV-2 antigen assay, compared to RT-PCR. Results: The prevalence of SARS-CoV-2 of the cohort was 4.5% with RT-PCR and 4.1% with LumiraDx antigen test. Compared to the RT-PCR, the sensitivity and specificity of the LumiraDx antigen SARS-CoV-2 test were 82,7% [95% CI 74.1-89,7] and 99.9% [95% CI 99.6-99.9] respectively. Given the RT-PCR threshold cycle (Ct) range, the sensitivity was 92.1% [95% CI 84.6-96.3] when the Ct value was below or equal 33 cycles, and 38.1% [95% CI 18.9-61.3] when it was above 33 cycles. The inter-rater reliability showed a kappa coefficient of 0.88 when considering all the patients and 0.94 for Ct values below 33 cycles. Conclusion: Our data have shown that the LumiraDx platform can be considered for large-scale testing of SARS-CoV-2.
, Soma Shirakabe, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin'ichiro Morita, Toshiyuki Yabe, Yoshihiro Kanehara, et al.
Published: 20 July 2017
Abstract:
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.
Yun R. Fu
Abstract:
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.
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