Anna Bernasconi

Convergent evolution in protein antigens is common across pathogens, including SARS-CoV-2; the most likely reason is the need to evade the selective pressure exerted by previous infection- or vaccine-elicited immunity. There is a pressing need for automated analysis of convergent mutations. We developed ConvMut, a tool to identify patterns of recurrent mutations in SARS-CoV-2 evolution; we exploited the granular phylogeny-based lineage hierarchy developed by PANGO, allowing us to observe deltas, i.e., groups of mutations that are acquired with respect to the immediately upstream tree nodes. Deltas comprise amino acid substitutions, insertions, and deletions. ConvMut can perform individual protein analysis to identify the most common single mutations acquired independently in a given subtree. Lineages are then gathered into clusters according to user-selected sets of shared mutations. An interactive graph orders the evolutionary steps of clusters, details the acquired amino acid change for each sublineage, and allows us to trace the evolutionary path until a selected lineage. ConvMut also supports frequency analysis for a given nucleotide or amino acid changes at a given residue across a selected phylogenetic subtree. ConvMut facilitates the exploration of convergent evolutionary trends in SARS-CoV-2, providing insights that could support the development of broadly effective anti-Spike monoclonal antibodies and Spike-based vaccines.

Reversion of amino acid mutations in structural proteins is common in viral evolution. SARS-CoV-2 provides an unprecedented opportunity for ecological studies, thanks to the abundance of available whole genome sequences. YoyoMut allows regular scanning of open SARS-CoV-2 data, reporting on all cyclic and reverting mutations within all proteins (including Spike), with fine-grained trend visualization distinguishing non-mutated from mutated positions (either fixated or cyclically reversed). In the whole CoVSpectrum database, order of 100 reverting and 50 fixated mutations were identified on Spike. Classification is determined using alternative algorithms (based on threshold or slope inversion); finally, a 3D-protein structure allows us to identify spatial clustering of adjacent mutated positions. Systematic, automated monitoring of these behaviors aids immunologists and structuralists in their manual curation. By generating informative reports, our tool supports daily activities that have practical implications for vaccine and therapeutic anti-Spike monoclonal antibody design: prioritizing analysis of cyclic mutation and reversion models could help avoid the recent failures in their development and inform future strategies.

Navigating extensive legislative corpora is often impeded by the linguistic complexity inherent in legal texts. To address this, we present a novel topic representation learning method designed to facilitate the systematic exploration of legislative content. We demonstrate the efficacy of this approach by applying it to the vast corpus of Italian legislation comprising about 74 k laws with more than 300 k articles. While current topic models group documents by latent semantic similarity, they often lack the granularity required for precise navigation. Our approach augments these representations by integrating our topic modeling framework with multi-label profiles. We enrich the representation of individual laws by extracting and ranking the top 10 keywords based on their relevance to the enclosing topic, subsequently aggregating these rankings to construct a comprehensive, alternative description of the broader legal themes. By bridging latent semantic clusters with explicit, LLM-generated labels, this method yields a highly interpretable representation of the corpus, significantly enhancing the profiling and navigability of complex legislative content. We improve over our baseline representation in 74.67% of cases, showing potential for re-use in highly specialized text corpora.

This editorial summarizes the content of the Special Issue on Data quality dimensions in Data FAIRification design and processes of the Journal of Data and Information Quality (JDIQ).

Highly pathogenic avian influenza H5N1 rarely infects mammals. In 2024-2025, however, genotypes B3.13 and D1.1 caused two independent spillovers into U.S. dairy cattle. By analysing 26,930 complete H5N1 genomes from global surveillance, we identified 73 major viral groups, most of which show continent-specific distribution in Europe, Asia, Africa, and North America. North American viruses exhibit higher genetic diversity in specific viral segments, including variants potentially associated with mammalian adaptation. Both dairy-cattle-associated B3.13 and D1.1 genotypes originate from the same geographic macro-area, suggesting a possible regional hotspot where avian-mammalian interfaces may facilitate viral adaptation. Our findings place the U.S. outbreaks in a global framework and indicate that North American H5N1 may be predisposed to cross-species transmission.