Intranasal SARS-CoV-2 RBD decorated nanoparticle vaccine enhances viral clearance in the Syrian hamster model.

Multiple vaccines have been developed and licensed for SARS-CoV-2. While these vaccines reduce disease severity, they do not prevent infection, and SARS-CoV-2 continues to spread and evolve. To prevent infection and limit transmission, vaccines must be developed that induce immunity in the respiratory tract. Therefore, we performed proof-of-principle vaccination studies with an intranasal nanoparticle vaccine against SARS-CoV-2. The vaccine candidate consisted of the self-assembling 60-subunit I3-01 protein scaffold covalently decorated with the SARS-CoV-2 receptor binding domain (RBD) using the SpyCatcher-SpyTag system. We verified the intended antigen display features by reconstructing the I3-01 scaffold to 3.4A using cryo-EM, and then demonstrated that the scaffold was highly saturated when grafted with RBD. Using this RBD-grafted SpyCage scaffold (RBD+SpyCage), we performed two unadjuvanted intranasal vaccination studies in the "gold-standard" preclinical Syrian hamster model. Hamsters received two vaccinations 28 days apart, and were then challenged 28 days post-boost with SARS-CoV-2. The initial study focused on assessing the immunogenicity of RBD+SpyCage, which indicated that vaccination of hamsters induced a non-neutralizing antibody response that enhanced viral clearance but did not prevent infection. In an expanded study, we demonstrated that covalent bonding of RBD to the scaffold was required to induce an antibody response. Consistent with the initial study, animals vaccinated with RBD+SpyCage more rapidly cleared SARS-CoV-2 from both the upper and lower respiratory tract. These findings demonstrate the intranasal SpyCage vaccine platform can induce protection against SARS-CoV-2 and, with additional modifications to improve immunogenicity, is a versatile platform for the development of intranasal vaccines targeting respiratory pathogens.

Immune durability and protection against SARS-CoV-2 re-infection in Syrian hamsters.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a pandemic. As immunity to endemic human coronaviruses (i.e. NL63 or OC43) wanes leading to re-infection, it was unknown if SARS-CoV-2 immunity would also decline permitting repeat infections. Recent case reports confirm previously infected individuals can become re-infected; however, re-infection may be due to heterogeneity in the initial infection or the host immune response, or may be the result of infection with a variant strain that escapes pre-existing immunity. To control these variables, we utilized the Syrian hamster model to evaluate the duration of immunity and susceptibility to re-infection with SARS-CoV-2. Hamsters were given a primary mock or SARS-CoV-2 infection (culture media or 105 TCID50 USA/WA1/2020 isolate, respectively). Mock and SARS-CoV-2 infected hamsters were then given a secondary SARS-CoV-2 infection at 1, 2, 4, or 6 months post-primary infection (n = 14/time point/group). After the primary SARS-CoV-2 infection, hamsters developed anti-spike protein IgG, IgA, and neutralizing antibodies, and these antibodies were maintained for at least 6 months. Upon secondary SARS-CoV-2 challenge, previously SARS-CoV-2 infected animals were protected from weight loss, while all previously mock-infected animals became infected and lost weight. Importantly, despite having high titres of antibodies, one SARS-CoV-2 infected animal re-challenged at 4 months had a breakthrough infection with replicating virus in the upper and lower respiratory tract. These studies demonstrate immunity to SARS-CoV-2 is maintained for 6 months; however, protection may be incomplete and, even in the presence of high antibody titres, previously infected hosts may become re-infected.

The fitness of drug-resistant malaria parasites in a rodent model: multiplicity of infection.

Malaria infections normally consist of more than one clonally replicating lineage. Within-host interactions between sensitive and resistant parasites can have profound effects on the evolution of drug resistance. Here, using the Plasmodium chabaudi mouse malaria model, we ask whether the costs and benefits of resistance are affected by the number of co-infecting strains competing with a resistant clone. We found strong competitive suppression of resistant parasites in untreated infections and marked competitive release following treatment. The magnitude of competitive suppression depended on competitor identity. However, there was no overall effect of the diversity of susceptible parasites on the extent of competitive suppression or release. If these findings generalize, then transmission intensity will impact on resistance evolution because of its effect on the frequency of mixed infections, not because of its effect on the distribution of clones per host. This would greatly simplify the computational problems of adequately capturing within-host ecology in models of drug resistance evolution in malaria.

Two-dimensional object alignment based on the robust oriented Hausdorff similarity measure.

This paper proposes an oriented Hausdorff similarity (OHS) measure for robust object alignment. The OHS measure is introduced by replacing the distance concept of conventional Hausdoff distance (HD) algorithms by the similarity concept of the Hough transform (HT). The proposed algorithm can be considered as the modified directed HT using the distance transform (DT). The orientation information at each pixel is also used to remove incorrect correspondences.

Object matching algorithms using robust Hausdorff distance measures.

A Hausdorff distance (HD) is one of commonly used measures for object matching. This work analyzes the conventional HD measures and proposes two robust HD measures based on m-estimation and least trimmed square (LTS) which are more efficient than the conventional HD measures. By computer simulation, the matching performance of the conventional and proposed HD measures is compared with synthetic and real images.

Hybrid estimation of navigation parameters from aerial image sequence.

This work presents a hybrid method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed hybrid system is composed of two parts: relative position estimation and absolute position estimation. Computer simulation with two different sets of real aerial image sequences shows the effectiveness of the proposed hybrid parameter estimation algorithm.