Virtual social networks and health technologies in the daily life of clients and households: care and health promotion.

This study aimed to understand the use of technosociality in the daily lives of the Family Health Strategy clients during the COVID-19 pandemic for care and health promotion. This holistic, qualitative, multiple case study based on the Comprehensive Everyday Life Sociology was conducted with 61 clients from three Brazilian municipalities, two in Minas Gerais and one in Santa Catarina. The sources of evidence were the open-ended individual interview and field notes.

epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.

The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests and antibody production. Several computational approaches have been proposed to identify, from an antigen protein or peptide sequence, which residues are more likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could otherwise be obtained. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well-established Cutoff Scanning Matrix algorithm and Organism Ontology information.

epitope3D: a machine learning method for conformational B-cell epitope prediction.

The ability to identify antigenic determinants of pathogens, or epitopes, is fundamental to guide rational vaccine development and immunotherapies, which are particularly relevant for rapid pandemic response. A range of computational tools has been developed over the past two decades to assist in epitope prediction; however, they have presented limited performance and generalization, particularly for the identification of conformational B-cell epitopes. Here, we present epitope3D, a novel scalable machine learning method capable of accurately identifying conformational epitopes trained and evaluated on the largest curated epitope data set to date.