Ice algal bloom development on the surface of the Greenland Ice Sheet

It is fundamental to understand the development of Zygnematophycean (Streptophyte) micro-algal blooms within Greenland Ice Sheet (GrIS) supraglacial environments, given their potential to significantly impact both physical (melt) and chemical (carbon and nutrient cycling) surface characteristics. Here, we report on a space-for-time assessment of a GrIS ice algal bloom, achieved by sampling an ∼85 km transect spanning the south-western GrIS bare ice zone during the 2016 ablation season. Cell abundances ranged from 0 to 1.6 × 104 cells ml−1, with algal biomass demonstrated to increase in surface ice with time since snow line retreat (R2 = 0.73, P < 0.05). A suite of light harvesting and photo-protective pigments were quantified across transects (chlorophylls, carotenoids and phenols) and shown to increase in concert with algal biomass. Ice algal communities drove net autotrophy of surface ice, with maximal rates of net production averaging 0.52 ± 0.04 mg C l−1 d−1, and a total accumulation of 1.306 Gg C (15.82 ± 8.14 kg C km−2) predicted for the 2016 ablation season across an 8.24 × 104 km2 region of the GrIS. By advancing our understanding of ice algal bloom development, this study marks an important step toward projecting bloom occurrence and impacts into the future

Determination of the uptake and translocation of nitrogen applied at different growth stages of a melon crop (Cucumis melo L.) using 15N isotope.

In order to establish a rational nitrogen (N) fertilisation and reduce groundwater contamination, a clearer understanding of the N distribution through the growing season and its dynamics inside the plant is crucial. In two successive years, a melon crop (Cucumis melo L. cv. Sancho) was grown under field conditions to determine the uptake of N fertiliser, applied by means of fertigation at different stages of plant growth, and to follow the translocation of N in the plant using 15N-labelled N. In 2006, two experiments were carried out. In the first experiment, labelled 15N fertiliser was supplied at the female-bloom stage and in the second, at the end of fruit ripening. Labelled 15N fertiliser was made from 15NH415NO3 (10 at.% 15N) and 9.6 kg N ha−1 were applied in each experiment over 6 days (1.6 kg N ha−1 d−1). In 2007, the 15N treatment consisted of applying 20.4 kg N ha−1 as 15NH415NO3 (10 at.% 15N) in the middle of fruit growth, over 6 days (3.4 kg N ha−1 d−1). In addition, 93 and 95 kg N ha−1 were supplied daily by fertigation as ammonium nitrate in 2006 and 2007, respectively. The results obtained in 2006 suggest that the uptake of N derived from labelled fertiliser by the above-ground parts of the plants was not affected by the time of fertiliser application. At the female-flowering and fruit-ripening stages, the N content derived from 15N-labelled fertiliser was close to 0.435 g m−2 (about 45% of the N applied), while in the middle of fruit growth it was 1.45 g m−2 (71% of the N applied). The N application time affected the amount of N derived from labelled fertiliser that was translocated to the fruits. When the N was supplied later, the N translocation was lower, ranging between 54% at female flowering and 32% at the end of fruit ripening. Approximately 85% of the N translocated came from the leaf when the N was applied at female flowering or in the middle of fruit growth. This value decreased to 72% when the 15N application was at the end of fruit ripening. The ammonium nitrate became available to the plant between 2 and 2.5 weeks after its application. Although the leaf N uptake varied during the crop cycle, the N absorption rate in the whole plant was linear, suggesting that the melon crop could be fertilised with constant daily N amounts until 2–3 weeks before the last harvest

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

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

Leveraging knowledge from historic engineering drawings

In the nuclear power industry, paper documents are in regular use as part of the life cycle of asset management, maintenance, and operations. Despite their importance, fully digitizing these documents is problematic due to being expensive and time consuming, while automation is made difficult by a combination of factors, primarily a significant time cost and required expertise by both software engineers and nuclear engineers to design digitization systems, label training data and validate the outputs of untrusted black-box solutions. In addition, a direct digitization of such documents, converting them to an equivalent digital format, is not necessarily the most useful representation of the contained knowledge. In this paper we present a framework for extracting and encoding both knowledge and data utilizing Knowledge Graphs (KGs); extracting the knowledge more efficiently than fully manual digitization, while still keeping the human supervision in place using a Human-in-the-loop approach. We then present a case study on using these techniques to convert engineering drawings into KGs, demonstrating the insights allowed by this form, as well as the advantages it provides to the development of intelligent systems. We show how this form can still be used to rebuild the original document in an equivalent format if desired, as well as how the accessible format makes it easier to analyze the data and develop a variety of other useful applications, such as tying in to other software packages or automatically suggesting redesigns

Fine mapping of genetic variants in BIN1, CLU, CR1 and PICALM for association with cerebrospinal fluid biomarkers for Alzheimer's disease.

Recent genome-wide association studies of Alzheimer's disease (AD) have identified variants in BIN1, CLU, CR1 and PICALM that show replicable association with risk for disease. We have thoroughly sampled common variation in these genes, genotyping 355 variants in over 600 individuals for whom measurements of two AD biomarkers, cerebrospinal fluid (CSF) 42 amino acid amyloid beta fragments (Aβ42) and tau phosphorylated at threonine 181 (ptau181), have been obtained. Association analyses were performed to determine whether variants in BIN1, CLU, CR1 or PICALM are associated with changes in the CSF levels of these biomarkers. Despite adequate power to detect effects as small as a 1.05 fold difference, we have failed to detect evidence for association between SNPs in these genes and CSF Aβ42 or ptau181 levels in our sample. Our results suggest that these variants do not affect risk via a mechanism that results in a strong additive effect on CSF levels of Aβ42 or ptau181