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Developing a cost-effective and environmentally benign substitute for the energy-intensive Haber-Bosch process for the production of ammonia is a global challenge. Electrocatalytic nitrogen reduction reaction (NRR) under ambient condition through...

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real-time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in O(|V | + |E|) time to create an initial cover before using modularity maximization to get the final cover.

The clinical manifestation of the recent pandemic COVID-19, caused by novel SARS-CoV-2, varies from mild to severe respiratory illness. Although environmental, demographic and co-morbidity factors have an impact on the severity of the disease, the contribution of mutations in each of the viral genes towards the degree of severity needs to be elucidated for designing better therapeutic approach against COVID-19. Here, we studied the effect of two substitutions D155Y and S171L, of ORF3a protein, found in COVID-19 patients. Using computational simulations we discovered that the substitutions at 155th and 171st positions changed the amino acids involved in salt bridge formation, hydrogen-bond occupancy, interactome clusters, and the stability of the protein. Protein-protein docking using HADDOCK analysis revealed that out of the two observed substitutions, only the substitution of D155Y, weakened the binding affinity of ORF3a with caveolin-1. The increased fluctuation in the simulated ORF3a-caveolin-1 complex suggested a change in the virulence property of SARS-CoV-2.