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

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

De hardnekkige mythe dat 'niks werkt' in de gesloten jeugdzorg: aanbevelingen voor professionals

In the Netherlands, a debate about effectiveness of secure residential youth care is currently being conducted. Some researchers conclude that ‘nothing can work’ in secure residential youth care, and argue that non-residential forms of treatment are superior to secure residential treatment. This article reviews recent research on this topic, and concludes that evidence for the effectiveness of non-residential treatment for adolescents with severe behavioural and/or criminal problems is lacking if considered as an alternative for secure residential youth care, whereas secure residential treatment shows a modest, but positive effect. In Nederland is momenteel een debat gaande over de effectiviteit van de gesloten residentiele jeugdzorg (Jeugdzorg Plus en Justitiele jeugdzorg). Sommige onderzoekers concluderen dat ‘niets kan werken in de gesloten jeugdzorg’ en pleiten voor interventies in het gezin. Dit artikel beschouwt het recente onderzoek op dit terrein en concludeert dat er nog onvoldoende empirisch bewijs is voor de effectiviteit van nietresidentiele jeugdzorginterventies voor adolescenten met ernstige gedragsproblemen en/of crimineel gedrag als alternatief voor residentiele jeugdzorginterventies. De effectiviteit van de gesloten residentiele jeugdzorg lijkt bescheiden, maar positief

Spectrometric differentiation of yeast strains using minimum volume increase and minimum direction change clustering criteria

This paper proposes new clustering criteria for distinguishing Saccharomyces cerevisiae (yeast) strains using their spectrometric signature. These criteria are introduced in an agglomerative hierarchical clustering context, and consist of: (a) minimizing the total volume of clusters, as given by their respective convex hulls; and, (b) minimizing the global variance in cluster directionality. The method is deterministic and produces dendrograms, which are important features for microbiologists. A set of experiments, performed on yeast spectrometric data and on synthetic data, show the new approach outperforms several well-known clustering algorithms, including techniques commonly used for microorganism differentiation.This work was supported by FEDER funds through Programa Operacional Factores de Competitividade - COMPETE, by national funds of the projects PEst-OE/EEI/LA0009/2013, PEst-OE/MAT/UI0152, PDTC/AGR-ALI/103392 and PDCTE/BIO/69310/2006 from the Fundacao para a Ciencia e a Tecnologia (FCT) and partially funded with Grant SFRH/BD/48310/2008 also from FCT