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A universal influenza vaccine is required for broad protection against influenza infection. Here, we revealed the efficacy of novel influenza vaccine candidates based on Ebola glycoprotein (EboGP) DC-targeting domain (EΔM) fusion protein technology. We fused influenza hemagglutinin stalk (HAcs) and extracellular matrix protein (M2e) or four copies of M2e (referred to as tetra M2e (tM2e)) with EΔM to generate EΔM-HM2e or EΔM-tM2e, respectively, and revealed that EΔM facilitates DC/macrophage targeting in vitro. In a mouse study, EΔM-HM2e- or EΔM-tM2e-pseudotyped viral particles (PVPs) induced significantly higher titers of anti-HA and/or anti-M2e antibodies. We also developed recombinant vesicular stomatitis virus (rVSV)-EΔM-HM2e and rVSV-EΔM-tM2e vaccines that resulted in rapid and potent induction of HA and/or M2 antibodies in mouse sera and mucosa. Importantly, vaccination protects mice from influenza H1N1 and H3N2 challenges. Taken together, our study suggests that recombinant rVSV-EΔM-HM2e and rVSV-EΔM-tM2e are efficacious and protective universal vaccines against influenza.

Until now, no approved effective vaccine and antiviral therapeutic are available for treatment or prevention of SARS-coronavirus 2 (SCoV-2) virus infection. In this study, we established a SCoV-2 Spike glycoprotein (SP), including a SP mutant D614G, pseudotyped HIV-1-based vector system and tested their ability to infect ACE2-expressing cells. This study revealed that a C-terminal 17 amino acid deletion in SCoV-2 SP significantly increases the incorporation of SP into the pseudotyped viruses and enhanced its infectivity, which may be helpful in the design of SCoV2-SP-based vaccine strategies. Moreover, based on this system, we have demonstrated that an aqueous extract from the Chinese herb Prunella vulgaris (CHPV) and a compound, suramin, displayed potent inhibitory effects on both wild type and mutant (G614) SCoV-2 SP pseudotyped virus (SCoV-2-SP-PVs)-mediated infection. The 50% inhibitory concentration (IC50) for CHPV and suramin on SCoV-2-SP-PVs are 30, and 40 μg/ml, respectively. To define the mechanisms of their actions, we demonstrated that both CHPV and suramin are able to directly interrupt SCoV-2–SP binding to its receptor ACE2 and block the viral entry step. Importantly, our results also showed that CHPV or suramin can efficiently reduce levels of cytopathic effect caused by SARS-CoV-2 virus (hCoV-19/Canada/ON-VIDO-01/2020) infection in Vero cells. Furthermore, our results demonstrated that the combination of CHPV/suramin with an anti-SARS-CoV-2 neutralizing antibody mediated more potent blocking effect against SCoV2-SP-PVs. Overall, this study provides evidence that CHPV and suramin has anti-SARS-CoV-2 activity and may be developed as a novel antiviral approach against SARS-CoV-2 infection.

Until now, antiviral therapeutic agents are still urgently required for treatment or prevention of SARS-coronavirus 2 (SCoV-2) virus infection. In this study, we established a sensitive SCoV-2 Spike glycoprotein (SP), including an SP mutant D614G, pseudotyped HIV-1-based vector system and tested their ability to infect ACE2-expressing cells. Based on this system, we have demonstrated that an aqueous extract from the Natural herb Prunella vulgaris (NhPV) displayed potent inhibitory effects on SCoV-2 SP (including SPG614 mutant) pseudotyped virus (SCoV-2-SP-PVs) mediated infections. Moreover, we have compared NhPV with another compound, Suramin, for their anti-SARS-CoV-2 activities and the mode of their actions, and found that both NhPV and Suramin are able to directly interrupt SCoV-2–SP binding to its receptor ACE2 and block the viral entry step. Importantly, the inhibitory effects of NhPV and Suramin were confirmed by the wild type SARS-CoV-2 (hCoV-19/Canada/ON-VIDO-01/2020) virus infection in Vero cells. Furthermore, our results also demonstrated that the combination of NhPV/Suramin with an anti-SARS-CoV-2 neutralizing antibody mediated a more potent blocking effect against SCoV2-SP-PVs. Overall, by using SARS-CoV-2 SP-pseudotyped HIV-1-based entry system, we provide strong evidence that NhPV and Suramin have anti-SARS-CoV-2 activity and may be developed as a novel antiviral approach against SARS-CoV-2 infection.

In this paper, the problem of non-rigid shape recognition is studied from the perspective of metric geometry. In particular, we explore the applicability of diffusion distances within the Gromov-Hausdorff framework. While the traditionally used geodesic distance exploits the shortest path between points on the surface, the diffusion distance averages all paths connecting the points. The diffusion distance constitutes an intrinsic metric which is robust, in particular, to topological changes. Such changes in the form of shortcuts, holes, and missing data may be a result of natural non-rigid deformations as well as acquisition and representation noise due to inaccurate surface construction. The presentation of the proposed framework is complemented with examples demonstrating that in addition to the relatively low complexity involved in the computation of the diffusion distances between surface points, its recognition and matching performances favorably compare to the classical geodesic distances in the presence of topological changes between the non-rigid shapes.

Sparse representations of signals, in particular with learned dictionaries, are widely used for state-of-the-art audio, image, and video restoration. In this paper, the problem of denoising and occlusion restoration of 3D range data based on dictionary learning and sparse representations is explored. We consider the 3D surface obtained from a desktop range scanner as an image, where the value of each pixel represents the depth of a point on the 3D surface. Having this image, we apply techniques from dictionary learning and sparse representation to enhance the acquired 3D surface. These techniques use the spare decomposition of the overlapping patches in the image, over an adapted over-complete dictionary, for enhancing the data. We present experimental results of denoising 3D surfaces following this approach. We also propose an algorithm for filling the missing information regions on 3D scans and demonstrate its effectiveness. Our experimental results are on range data obtained from a low-cost structured-light range scanner.

A geometric framework for the recognition of three-dimensional objects represented by point clouds is introduced in this paper. The proposed approach is based on comparing distributions of intrinsic measurements on the point cloud. In particular, intrinsic distances are exploited as signatures for representing the point clouds. The first signature we introduce is the histogram of pairwise diffusion distances between all points on the shape surface. These distances represent the probability of traveling from one point to another in a fixed number of random steps, the average intrinsic distances of all possible paths of a given number of steps between the two points. This signature is augmented by the histogram of the actual pairwise geodesic distances in the point cloud, the distribution of the ratio between these two distances, as well as the distribution of the number of times each point lies on the shortest paths between other points. These signatures are not only geometric but also invariant to bends. We further augment these signatures by the distribution of a curvature function and the distribution of a curvature weighted distance. These histograms are compared using the @g^2 or other common distance metrics for distributions. The presentation of the framework is accompanied by theoretical and geometric justification and state-of-the-art experimental results with the standard Princeton 3D shape benchmark, ISDB, and nonrigid 3D datasets. We also present a detailed analysis of the particular relevance of each one of the different proposed histogram-based signatures. Finally, we briefly discuss a more local approach where the histograms are computed for a number of overlapping patches from the object rather than the whole shape, thereby opening the door to partial shape comparisons.

In this note we propose to introduce physical constraints in the localization problem in sensor networks. This is based on extending the classical STRESS function from distance geometry and multidimensional scaling. We present the underlying framework and demonstrate its importance with three examples: penalizing the sensors for being in high elevation areas, removing sensors from forbidden areas, and forcing the sensors to be on pre-described curves. We also extend the work to dynamic environments.

In this note we show how to introduce physical constraints in the localization problem in sensor networks. This is based on extending the classical STRESS function from distance geometry and multidimensional scaling. We present the underlying framework and demonstrate it with three examples: penalizing the sensors for being in high elevation areas, removing sensors from forbidden areas, and forcing the sensors to be on pre-described curves.