The evolution of language: a comparative review

For many years the evolution of language has been seen as a disreputable topic, mired in fanciful "just so stories" about language origins. However, in the last decade a new synthesis of modern linguistics, cognitive neuroscience and neo-Darwinian evolutionary theory has begun to make important contributions to our understanding of the biology and evolution of language. I review some of this recent progress, focusing on the value of the comparative method, which uses data from animal species to draw inferences about language evolution. Discussing speech first, I show how data concerning a wide variety of species, from monkeys to birds, can increase our understanding of the anatomical and neural mechanisms underlying human spoken language, and how bird and whale song provide insights into the ultimate evolutionary function of language. I discuss the ‘‘descended larynx’ ’ of humans, a peculiar adaptation for speech that has received much attention in the past, which despite earlier claims is not uniquely human. Then I will turn to the neural mechanisms underlying spoken language, pointing out the difficulties animals apparently experience in perceiving hierarchical structure in sounds, and stressing the importance of vocal imitation in the evolution of a spoken language. Turning to ultimate function, I suggest that communication among kin (especially between parents and offspring) played a crucial but neglected role in driving language evolution. Finally, I briefly discuss phylogeny, discussing hypotheses that offer plausible routes to human language from a non-linguistic chimp-like ancestor. I conclude that comparative data from living animals will be key to developing a richer, more interdisciplinary understanding of our most distinctively human trait: language

Spatial and temporal epidemiology of SARS-CoV-2 virus lineages in Teesside, UK, in 2020: effects of socio-economic deprivation, weather, and lockdown on lineage dynamics

Background: SARS-CoV-2 emerged in the UK in January 2020. The UK government introduced control measures including national ‘lockdowns’ and local ‘tiers’ in England to control virus transmission. As the outbreak continued, new variants were detected through two national monitoring programmes that conducted genomic sequencing. This study aimed to determine the effects of weather, demographic features, and national and local COVID-19 restrictions on positive PCR tests at a sub-regional scale. Methods: We examined the spatial and temporal patterns of COVID-19 in the Teesside sub-region of the UK, from January to December 2020, capturing the first two waves of the epidemic. We used a combination of disease mapping and mixed-effect modelling to analyse the total positive tests, and those of the eight most common virus lineages, in response to potential infection risk factors: socio-economic deprivation, population size, temperature, rainfall, government interventions, and a government restaurant subsidy (“Eat Out to Help Out”). Results: Total positive tests of SARS-CoV-2 were decreased by temperature and the first national lockdown (the only one to include school closures), while deprivation, population, the second national lockdown, and the local tiered interventions were associated with increased cases. The restaurant subsidy and rainfall had no apparent effect. The relationships between positive tests and covariates varied greatly between lineages, likely due to the strong heterogeneity in their spatial and temporal distributions. Cases during the second wave appeared to be higher in areas that recorded fewer first-wave cases, however, an additional model showed the number of first-wave cases was not predictive of second-wave cases. Discussion: National and local government interventions appeared to be ineffective at the sub-regional level if they did not include school closures. Examination of viral lineages at the sub-regional scale was less useful in terms of investigating covariate associations but may be more useful for tracking spread within communities. Our study highlights the importance of understanding the effects of government interventions in local and regional contexts, and the importance of applying local restrictions appropriately within such settings.

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease