New vaccine modalities need to be developed that can activate more potently the immune system, in this regard, adjuvants augment adaptive immune responses and can improve vaccine performance. Aluminium salt (alum) is the most commonly used adjuvant for human vaccination. However, it drives primarily TH2-effector responses and is not effective for vaccines that target mucosal surfaces. Thus, safe and potent adjuvants need to be developed that can increase and direct vaccine-specific immunity. Recent advances in our understanding of innate immune responses are providing opportunities to design better adjuvants. The innate immune system senses microbes through pattern-recognition receptors (PPRs), which include the Toll-like receptors (TLRs), and intracellular NOD-like receptors (NLRs) and C-type lectin-like (CTLs) receptors that are expressed by immune cells. Activation of these receptors leads to the production of cytokines that provide early defences during infection. Cytokines also regulate adaptive immunity by controlling the quantity and quality of B and T cell activation, which in turn results in protective immune responses to pathogens. Pathogen-associated molecular patterns (PAMPs) such as lipopolysaccharides, lipopeptides, and peptidoglycan fragments can activate PPRs and are attractive compounds for the development of new adjuvant. Although during microbial infection many different PRRs are activated, almost all adjuvants that are being developed rely on the stimulation of a single PRR. In this project, we propose that compound adjuvants derived by the covalent linking of two PAMPs (fusion PAMPs), for example, TLR2 and NOD agonists, will ensure that immune cells are being exposed to both, resulting in efficient cross talk of signal transduction pathways and in synergistic immune activation. If so, chimeric immune modulators (fusion PAMPs) can be employed at lower adjuvant concentrations, thereby minimizing unwanted side effects.

Despite great progress in the field of Natural Language Processing (NLP), the field is still struggling to ensure the robustness and fairness of models. So far, NLP has prioritized data size over data quality. Yet there is growing evidence suggesting that the diversity of data, a key dimension of data quality, is crucial for fair and robust NLP models. Many researchers are therefore trying to create more diverse datasets, but there is no clear path for them to follow. Even the fundamental question “How can we measure the diversity of a dataset?” is currently wide open. It is both surprising and concerning that we still lack the tools and theoretical insights to understand, improve, and leverage data diversity in NLP. DataDivers will 1) develop the first ever framework to measure data diversity in NLP datasets; 2) investigate how data diversity impacts NLP model behavior; and 3) develop novel approaches that harness data diversity for fairer and more robust NLP models. I operationally define the diversity of a text collection as the variability of texts along specific dimensions (e.g., semantic, lexical, and sociolinguistic). Sociolinguistic diversity in particular, is an overlooked but crucial dimension, which I am committed to addressing. DataDivers will break new ground by taking a comprehensive view of data diversity, which is urgently needed for robust and fair NLP. Its approach will be both theoretical and empirical. It will combine insights from disciplines that have developed methodologies to quantify data diversity with rigorous empirical experimentation. DataDivers will take a unique view on data diversity: measuring it at the dataset level, and across contexts for individual features. Finally, DataDivers will use its framework to develop diversity-informed data collection and model training methods. DataDivers’ results will impact the full NLP development pipeline—from data collection to evaluation—and open up a new, urgently needed, area of research.