CoVizu: Rapid analysis and visualization of the global diversity of SARS-CoV-2 genomes.

Phylogenetics has played a pivotal role in the genomic epidemiology of severe acute respiratory syndrome coronavirus 2, such as tracking the emergence and global spread of variants and scientific communication. However, the rapid accumulation of genomic data from around the world-with over two million genomes currently available in the Global Initiative on Sharing All Influenza Data database-is testing the limits of standard phylogenetic methods. Here, we describe a new approach to rapidly analyze and visualize large numbers of SARS-CoV-2 genomes. Using Python, genomes are filtered for problematic sites, incomplete coverage, and excessive divergence from a strict molecular clock. All differences from the reference genome, including indels, are extracted using minimap2 and compactly stored as a set of features for each genome. For each Pango lineage (https://cov-lineages.org), we collapse genomes with identical features into 'variants', generate 100 bootstrap samples of the feature set union to generate weights, and compute the symmetric differences between the weighted feature sets for every pair of variants. The resulting distance matrices are used to generate neighbor-joining trees in RapidNJ that are converted into a majority-rule consensus tree for each lineage. Branches with support values below 50 per cent or mean lengths below 0.5 differences are collapsed, and tip labels on affected branches are mapped to internal nodes as directly sampled ancestral variants. Currently, we process about 2 million genomes in approximately 9 h on 52 cores. The resulting trees are visualized using the JavaScript framework D3.js as 'beadplots', in which variants are represented by horizontal line segments, annotated with beads representing samples by collection date. Variants are linked by vertical edges to represent branches in the consensus tree. These visualizations are published at https://filogeneti.ca/CoVizu. All source code was released under an MIT license at https://github.com/PoonLab/covizu.

CoVizu: Rapid analysis and visualization of the global diversity of SARS-CoV-2 genomes (preprint)

Phylogenetics has played a pivotal role in the genomic epidemiology of SARS-CoV-2, such as tracking the emergence and global spread of variants, and scientific communication. However, the rapid accumulation of genomic data from around the world - with over two million genomes currently available in the GISAID database - is testing the limits of standard phylogenetic methods. Here, we describe a new approach to rapidly analyze and visualize large numbers of SARS-CoV-2 genomes. Using Python, genomes are filtered for problematic sites, incomplete coverage, and excessive divergence from a strict molecular clock. All differences from the reference genome, including indels, are extracted using minimap2, and compactly stored as a set of features for each genome. For each Pango lineage (https://cov-lineages.org), we collapse genomes with identical features into 'variants', generate 100 bootstrap samples of the feature set union to generate weights, and compute the symmetric differences between the weighted feature sets for every pair of variants. The resulting distance matrices are used to generate neigihbor-joining trees in RapidNJ and converted into a majority-rule consensus tree for the lineage. Branches with support values below 50% or mean lengths below 0.5 differences are collapsed, and tip labels on affected branches are mapped to internal nodes as directly-sampled ancestral variants. Currently, we process about 1.6 million genomes in approximately nine hours on 34 cores. The resulting trees are visualized using the JavaScript framework D3.js as 'beadplots', in which variants are represented by horizontal line segments, annotated with beads representing samples by collection date. Variants are linked by vertical edges to represent branches in the consensus tree. These visualizations are published at https://filogeneti.ca/CoVizu. All source code was released under an MIT license at https://github.com/PoonLab/covizu.

EFFECT OF CONVALESCENT PLASMA ON MORTALITY IN PATIENTS WITH COVID-19 PNEUMONIA (preprint)

Abstract Background Convalescent plasma, widely utilized in viral infections that induce neutralizing antibodies, has been proposed for COVID-19, and preliminary evidence shows that it might have beneficial effect. Our objective was to compare epidemiological characteristics and outcomes between patients who received convalescent plasma for COVID-19 and those who did not, admitted to hospitals in Buenos Aires Province, Argentina, throughout the pandemic. Methods This is a multicenter, retrospective cohort study of 2-month duration beginning on June 1, 2020, including unselected, consecutive adult patients with diagnosed COVID-19, admitted to 215 hospitals with pneumonia. Epidemiological and clinical variables were registered in the Provincial Hospital Bed Management System. Convalescent plasma was supplied as part of a centralized, expanded access program. Results We analyzed 3,529 patients with pneumonia, predominantly male, aged 62{+/-}17, with arterial hypertension and diabetes as main comorbidities; 51.4% were admitted to the ward, 27.1% to the Intensive Care Unit (ICU), and 21.7% to the ICU with mechanical ventilation requirement (ICU-MV). 28-day mortality was 34.9%; and was 26.3%, 30.1% and 61.4% for ward, ICU and ICU-MV patients. Convalescent plasma was administered to 868 patients (24.6%); their 28-day mortality was significantly lower (25.5% vs. 38.0%, p<0.001). No major adverse effects occurred. Logistic regression analysis identified age, ICU admission with and without MV requirement, diabetes and preexistent cardiovascular disease as independent predictors of 28-day mortality, whereas convalescent plasma administration acted as a protective factor. Conclusions Our study suggests that the administration of convalescent plasma in COVID-19 pneumonia admitted to the hospital might be associated with decreased mortality.

Language comprehension in the social brain: Electrophysiological brain signals of social presence effects during syntactic and semantic sentence processing

Although, evolutionarily, language emerged predominantly for social purposes, much has yet to be uncovered regarding how language processing is affected by social context. Social presence research studies the ways in which the presence of a conspecific affects processing, but has yet to be thoroughly applied to language processes. The principal aim of this study was to see how syntactic and semantic language processing might be subject to mere social presence effects by studying Event-Related brain Potentials (ERP). In a sentence correctness task, participants read sentences with a semantic or syntactic anomaly while being either alone or in the mere presence of a confederate. Compared to the alone condition, the presence condition was associated with an enhanced N400 component and a more centro-posterior LAN component (interpreted as an N400). The results seem to imply a boosting of heuristic language processing strategies, proper of lexico-semantic operations, which actually entails a shift in the strategy to process morphosyntactic violations, typically based on algorithmic or rule-based strategies. The effects cannot be related to increased arousal levels. The apparent enhancement of the activity in the precuneus while in presence of another person suggests that the effects conceivably relate to social cognitive and attentional factors. The present results suggest that understanding language comprehension would not be complete without considering the impact of social presence effects, inherent to the most natural and fundamental communicative scenarios.