INDIGO: a generalized model and framework for performance prediction of data dissemination

According to recent studies, an enormous rise in location-based mobile services is expected in future. People are interested in getting and acting on the localized information retrieved from their vicinity like local events, shopping offers, local food, etc. These studies also suggested that local businesses intend to maximize the reach of their localized offers/advertisements by pushing them to the maxi- mum number of interested people. The scope of such localized services can be augmented by leveraging the capabilities of smartphones through the dissemination of such information to other interested people. To enable local businesses (or publishers) of localized services to take in- formed decision and assess the performance of their dissemination-based localized services in advance, we need to predict the performance of data dissemination in complex real-world scenarios. Some of the questions relevant to publishers could be the maximum time required to disseminate information, best relays to maximize information dissemination etc. This thesis addresses these questions and provides a solution called INDIGO that enables the prediction of data dissemination performance based on the availability of physical and social proximity information among people by collectively considering different real-world aspects of data dissemination process. INDIGO empowers publishers to assess the performance of their localized dissemination based services in advance both in physical as well as the online social world. It provides a solution called INDIGO–Physical for the cases where physical proximity plays the fundamental role and enables the tighter prediction of data dissemination time and prediction of best relays under real-world mobility, communication and data dissemination strategy aspects. Further, this thesis also contributes in providing the performance prediction of data dissemination in large-scale online social networks where the social proximity is prominent using INDIGO–OSN part of the INDIGO framework under different real-world dissemination aspects like heterogeneous activity of users, type of information that needs to be disseminated, friendship ties and the content of the published online activities. INDIGO is the first work that provides a set of solutions and enables publishers to predict the performance of their localized dissemination based services based on the availability of physical and social proximity information among people and different real-world aspects of data dissemination process in both physical and online social networks. INDIGO outperforms the existing works for physical proximity by providing 5 times tighter upper bound of data dissemination time under real-world data dissemination aspects. Further, for social proximity, INDIGO is able to predict the data dissemination with 90% accuracy and differently, from other works, it also provides the trade-off between high prediction accuracy and privacy by introducing the feature planes from an online social networks

Trends of humoral immune responses to heterologous antigenic exposure due to vaccination & omicron SARS-CoV-2 infection: Implications for boosting

Background & objectives: Vaccination and natural infection can both augment the immune responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but how omicron infection has affected the vaccine-induced and hybrid immunity is not well studied in Indian population. The present study was aimed to assess the durability and change in responses of humoral immunity with age, prior natural infection, vaccine type and duration with a minimum gap of six months post-two doses with either ChAdOx1 nCov-19 or BBV152 prior- and post-emergence of the omicron variant. Methods: A total of 1300 participants were included in this observational study between November 2021 and May 2022. Participants had completed at least six months after vaccination (2 doses) with either ChAdOx1 nCoV-19 or an inactivated whole virus vaccine BBV152. They were grouped according to their age (≤ or ≥60 yr) and prior exposure of SARS-CoV-2 infection. Five hundred and sixteen of these participants were followed up after emergence of the Omicron variant. The main outcome was durability and augmentation of the humoral immune response as determined by anti-receptor-binding domain (RBD) immunoglobulin G (IgG) concentrations, anti-nucleocapsid antibodies and anti-omicron RBD antibodies. Live virus neutralization assay was conducted for neutralizing antibodies against four variants – ancestral, delta and omicron and omicron sublineage BA.5. Results: Before the omicron surge, serum anti-RBD IgG antibodies were detected in 87 per cent participants after a median gap of eight months from the second vaccine dose, with a median titre of 114 [interquartile range (IQR) 32, 302] BAU/ml. The levels increased to 594 (252, 1230) BAU/ml post-omicron surge (P<0.001) with 97 per cent participants having detectable antibodies, although only 40 had symptomatic infection during the omicron surge irrespective of vaccine type and previous history of infection. Those with prior natural infection and vaccination had higher anti-RBD IgG titre at baseline, which increased further [352 (IQR 131, 869) to 816 (IQR 383, 2001) BAU/ml] (P<0.001). The antibody levels remained elevated after a mean time gap of 10 months, although there was a decline of 41 per cent. The geometric mean titre was 452.54, 172.80, 83.1 and 76.99 against the ancestral, delta, omicron and omicron BA.5 variants in the live virus neutralization assay. Interpretation & conclusions: Anti-RBD IgG antibodies were detected in 85 per cent of participants after a median gap of eight months following the second vaccine dose. Omicron infection probably resulted in a substantial proportion of asymptomatic infection in the first four months in our study population and boosted the vaccine-induced humoral immune response, which declined but still remained durable over 10 months

A broadly neutralizing monoclonal antibody overcomes the mutational landscape of emerging SARS-CoV-2 variants of concern.

The emergence of new variants of SARS-CoV-2 necessitates unremitting efforts to discover novel therapeutic monoclonal antibodies (mAbs). Here, we report an extremely potent mAb named P4A2 that can neutralize all the circulating variants of concern (VOCs) with high efficiency, including the highly transmissible Omicron. The crystal structure of the P4A2 Fab:RBD complex revealed that the residues of the RBD that interact with P4A2 are a part of the ACE2-receptor-binding motif and are not mutated in any of the VOCs. The pan coronavirus pseudotyped neutralization assay confirmed that the P4A2 mAb is specific for SARS-CoV-2 and its VOCs. Passive administration of P4A2 to K18-hACE2 transgenic mice conferred protection, both prophylactically and therapeutically, against challenge with VOCs. Overall, our data shows that, the P4A2 mAb has immense therapeutic potential to neutralize the current circulating VOCs. Due to the overlap between the P4A2 epitope and ACE2 binding site on spike-RBD, P4A2 may also be highly effective against a number of future variants