Canine respiratory coronavirus employs caveolin-1-mediated pathway for internalization to HRT-18G cells

Canine respiratory coronavirus (CRCoV), identified in 2003, is a member of the Coronaviridae family. The virus is a betacoronavirus and a close relative of human coronavirus OC43 and bovine coronavirus. Here, we examined entry of CRCoV into human rectal tumor cells (HRT-18G cell line) by analyzing co-localization of single virus particles with cellular markers in the presence or absence of chemical inhibitors of pathways potentially involved in virus entry. We also targeted these pathways using siRNA. The results show that the virus hijacks caveolin-dependent endocytosis to enter cells via endocytic internalization

Canine Respiratory Coronavirus, Bovine Coronavirus, and Human Coronavirus OC43: Receptors and Attachment Factors

Despite high similarity of canine respiratory coronavirus (CRCoV), bovine coronavirus, (BCoV) and human coronavirus OC43 (HCoV-OC43), these viruses differ in species specificity. For years it was believed that they share receptor specificity, utilizing sialic acids for cell surface attachment, internalization, and entry. Interestingly, careful literature analysis shows that viruses indeed bind to the cell surface via sialic acids, but there is no solid data that these moieties mediate virus entry. In our study, using a number of techniques, we showed that all three viruses are indeed able to bind to sialic acids to a different extent, but these molecules render the cells permissive only for the clinical strain of HCoV-OC43, while for others they serve only as attachment receptors. CRCoV and BCoV appear to employ human leukocyte antigen class I (HLA-1) as the entry receptor. Furthermore, we identified heparan sulfate as an alternative attachment factor, but this may be related to the cell culture adaptation, as in ex vivo conditions, it does not seem to play a significant role. Summarizing, we delineated early events during CRCoV, BCoV, and HCoV-OC43 entry and systematically studied the attachment and entry receptor utilized by these viruses

Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.</jats:p