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Multiple SARS coronaviral proteins, such as ORF3b, ORF6, N, M, and nsp1, have been suggested to inhibit IFN production (Kamitani et al., 2006; Devaraj et al., 2007; Frieman et al., 2007; Kopecky-Bromberg et al., 2007; Wathelet et al., 2007; Zust et al., 2007; Narayanan et al., 2008; Siu et al., 2009).
One of the strengths of our study is that the exposure occurred during a defined period in a contained unit, and as such, there is less potential for confounding caused by the exposure of healthcare workers to multiple SARS patients.
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However, for the analysis of multiple SAR components that might exist in compound activity classes, systematic approaches are needed [2].
According to the ℓ0-norm minimization, the mixed-norm strategy is used to explore the common patterns in sparse representation coefficients of multiple SAR images.
Among the aforementioned studies, quite a lot of early works focus on merging building features from multiple SAR images or multi-sensor data.
Analysis of SAR graphs and global and local SARI scores facilitates the characterization of multiple SAR features both in qualitative and quantitative terms and aids in understanding how local SAR characteristics are related to each other.
In this study, we aim to demonstrate the considerable potential by using SAR imaging not only for single-amplitude analysis of L-band SAR but also for making use of (1) multi-temporal/polarization amplitude and (2) polarimetric signatures that are acquired through multiple SAR imaging passes.
The first stage is seeking the ℓ1-norm solutions for multiple-view SAR images via (5).
In the JSR model, multiple-view SAR images are integrated in a matrix form.
In practice, we can obtain multiple-view SAR images of the same physical target.
The IJSR model can effectively combine multiple-view SAR images from the same physical target to improve the recognition performance.
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