Genomic approaches to understanding population divergence and speciation in birds

© 2016 American Ornithologists\u27 Union. The widespread application of high-throughput sequencing in studying evolutionary processes and patterns of diversification has led to many important discoveries. However, the barriers to utilizing these technologies and interpreting the resulting data can be daunting for first-time users. We provide an overview and a brief primer of relevant methods (e.g., whole-genome sequencing, reduced-representation sequencing, sequence-capture methods, and RNA sequencing), as well as important steps in the analysis pipelines (e.g., loci clustering, variant calling, whole-genome and transcriptome assembly). We also review a number of applications in which researchers have used these technologies to address questions related to avian systems. We highlight how genomic tools are advancing research by discussing their contributions to 3 important facets of avian evolutionary history. We focus on (1) general inferences about biogeography and biogeographic history, (2) patterns of gene flow and isolation upon secondary contact and hybridization, and (3) quantifying levels of genomic divergence between closely related taxa. We find that in many cases, high-throughput sequencing data confirms previous work from traditional molecular markers, although there are examples in which genome-wide genetic markers provide a different biological interpretation. We also discuss how these new data allow researchers to address entirely novel questions, and conclude by outlining a number of intellectual and methodological challenges as the genomics era moves forward

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Analysis of shared heritability in common disorders of the brain

Paroxysmal Cerebral Disorder

Post-acute COVID-19 neuropsychiatric symptoms are not associated with ongoing nervous system injury

A proportion of patients infected with severe acute respiratory syndrome coronavirus 2 experience a range of neuropsychiatric symptoms months after infection, including cognitive deficits, depression and anxiety. The mechanisms underpinning such symptoms remain elusive. Recent research has demonstrated that nervous system injury can occur during COVID-19. Whether ongoing neural injury in the months after COVID-19 accounts for the ongoing or emergent neuropsychiatric symptoms is unclear. Within a large prospective cohort study of adult survivors who were hospitalized for severe acute respiratory syndrome coronavirus 2 infection, we analysed plasma markers of nervous system injury and astrocytic activation, measured 6 months post-infection: neurofilament light, glial fibrillary acidic protein and total tau protein. We assessed whether these markers were associated with the severity of the acute COVID-19 illness and with post-acute neuropsychiatric symptoms (as measured by the Patient Health Questionnaire for depression, the General Anxiety Disorder assessment for anxiety, the Montreal Cognitive Assessment for objective cognitive deficit and the cognitive items of the Patient Symptom Questionnaire for subjective cognitive deficit) at 6 months and 1 year post-hospital discharge from COVID-19. No robust associations were found between markers of nervous system injury and severity of acute COVID-19 (except for an association of small effect size between duration of admission and neurofilament light) nor with post-acute neuropsychiatric symptoms. These results suggest that ongoing neuropsychiatric symptoms are not due to ongoing neural injury