From GNNs to Sparse Transformers: Graph-based architectures for Multi-hop Question Answering

Sparse Transformers have surpassed Graph Neural Networks (GNNs) as the state-of-the-art architecture for multi-hop question answering (MHQA). Noting that the Transformer is a particular message passing GNN, in this paper we perform an architectural analysis and evaluation to investigate why the Transformer outperforms other GNNs on MHQA. We simplify existing GNN-based MHQA models and leverage this system to compare GNN architectures in a lower compute setting than token-level models. Our results support the superiority of the Transformer architecture as a GNN in MHQA. We also investigate the role of graph sparsity, graph structure, and edge features in our GNNs. We find that task-specific graph structuring rules outperform the random connections used in Sparse Transformers. We also show that utilising edge type information alleviates performance losses introduced by sparsity

Spleen tyrosine kinase mediates innate and adaptive immune crosstalk in SARS-CoV-2 mRNA vaccination

Durable cell-mediated immune responses require efficient innate immune signaling and the release of pro-inflammatory cytokines. How precisely mRNA vaccines trigger innate immune cells for shaping antigen specific adaptive immunity remains unknown. Here, we show that SARS-CoV-2 mRNA vaccination primes human monocyte-derived macrophages for activation of the NLRP3 inflammasome. Spike protein exposed macrophages undergo NLRP3-driven pyroptotic cell death and subsequently secrete mature interleukin-1β. These effects depend on activation of spleen tyrosine kinase (SYK) coupled to C-type lectin receptors. Using autologous cocultures, we show that SYK and NLRP3 orchestrate macrophage-driven activation of effector memory T cells. Furthermore, vaccination-induced macrophage priming can be enhanced with repetitive antigen exposure providing a rationale for prime-boost concepts to augment innate immune signaling in SARS-CoV-2 vaccination. Collectively, these findings identify SYK as a regulatory node capable of differentiating between primed and unprimed macrophages, which modulate spike protein-specific T cell responses

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication