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Deep-learning object detection methods that are designed for computer vision applications tend to under-perform when applied to remote sensing data. This is because, contrary to computer vision, in remote sensing training data are harder to collect and targets can be very small, occupying only a few pixels in the entire image, and exhibit arbitrary perspective transformations. Detection performance can improve by fusing data from multiple remote sensing modalities, including RGB, IR, hyper-spectral, multi-spectral, synthetic aperture radar, and LiDAR, to name a few. In this work, we propose YOLOrs: a new convolutional neural network, specifically designed for realtime object detection in multimodal remote sensing imagery. YOLOrs can detect objects at multiple scales, with smaller receptive fields to account for small targets, as well as predict target orientations. In addition, YOLOrs introduces a novel midlevel fusion architecture that renders it applicable to multimodal aerial imagery. Our experimental studies compare YOLOrs with contemporary alternatives and corroborate its merits.

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The emergence of novel variants of SARS-CoV-2 and their abilities to evade the immune response elicited through presently available vaccination makes it essential to recognize the mechanisms through which SARS-CoV-2 interacts with the human immune response. It is essential not only to comprehend the infection mechanism of SARS-CoV-2 but also for the generation of effective and reliable vaccines against COVID-19. The effectiveness of the vaccine is supported by the adaptive immune response, which mainly consists of B and T cells, which play a critical role in deciding the prognosis of the COVID-19 disease. T cells are essential for reducing the viral load and containing the infection. A plethora of viral proteins can be recognized by T cells and provide a broad range of protection, especially amid the emergence of novel variants of SARS-CoV-2. However, the hyperactivation of the effector T cells and reduced number of lymphocytes have been found to be the key characteristics of the severe disease. Notably, excessive T cell activation may cause acute respiratory distress syndrome (ARDS) by producing unwarranted and excessive amounts of cytokines and chemokines. Nevertheless, it is still unknown how T-cell-mediated immune responses function in determining the prognosis of SARS-CoV-2 infection. Additionally, it is unknown how the functional perturbations in the T cells lead to the severe form of the disease and to reduced protection not only against SARS-CoV-2 but many other viral infections. Hence, an updated review has been developed to understand the involvement of T cells in the infection mechanism, which in turn determines the prognosis of the disease. Importantly, we have also focused on the T cells’ exhaustion under certain conditions and how these functional perturbations can be modulated for an effective immune response against SARS-CoV-2. Additionally, a range of therapeutic strategies has been discussed that can elevate the T cell-mediated immune response either directly or indirectly.

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