Simonsenia aveniformis sp nov (Bacillariophyceae), molecular phylogeny and systematics of the genus, and a new type of canal raphe system

The genus Simonsenia is reviewed and S. aveniformis described as new for science by light and electron microscopy. The new species originated from estuarine environments in southern Iberia (Atlantic coast) and was isolated into culture. In LM, Simonsenia resembles Nitzschia, with bridges (fibulae) beneath the raphe, which is marginal. It is only electron microscope (EM) examination that reveals the true structure of the raphe system, which consists of a raphe canal raised on a keel (wing), supported by rib like braces (fenestral bars) and tube-like portulae; between the portulae the keel is perforated by open windows (fenestrae). Based on the presence of portulae and a fenestrated keel, Simonsenia has been proposed to be intermediate between Bacillariaceae and Surirellaceae. However, an rbcL phylogeny revealed that Simonsenia belongs firmly in the Bacillariaceae, with which it shares a similar chloroplast arrangement, rather than in the Surirellaceae. Lack of homology between the surirelloid and simonsenioid keels is reflected in subtle differences in the morphology and ontogeny of the portulae and fenestrae. The diversity of Simonsenia has probably been underestimated, particularly in the marine environment.Polish National Science Centre in Cracow within the Maestro program [N 2012/04/A/ST10/00544]; Sciences and Technologies Foundation-FCT (Portugal) [SFRH/BD/62405/2009]info:eu-repo/semantics/publishedVersio

An observation-based constraint on permafrost loss as a function of global warming

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordPermafrost, which covers 15 million km 2 of the land surface, is one of the components of the Earth system that is most sensitive to warming. Loss of permafrost would radically change high-latitude hydrology and biogeochemical cycling, and could therefore provide very significant feedbacks on climate change. The latest climate models all predict warming of high-latitude soils and thus thawing of permafrost under future climate change, but with widely varying magnitudes of permafrost thaw. Here we show that in each of the models, their present-day spatial distribution of permafrost and air temperature can be used to infer the sensitivity of permafrost to future global warming. Using the same approach for the observed permafrost distribution and air temperature, we estimate a sensitivity of permafrost area loss to global mean warming at stabilization of million km 2 °C â '1 (1σ confidence), which is around 20% higher than previous studies. Our method facilitates an assessment for COP21 climate change targets: if the climate is stabilized at 2 °C above pre-industrial levels, we estimate that the permafrost area would eventually be reduced by over 40%. Stabilizing at 1.5 °C rather than 2 °C would save approximately 2 million km 2 of permafrost.European Union Seventh Framework ProgrammeNatural Environment Research Council (NERC)Swedish Research CouncilResearch Council of NorwayUK DECC/Defra Met Office HadleyEuropean Unio

Evaluation of a Commercial Enzyme Linked Immunosorbent Assay (ELISA) for the Determination of the Neurotoxin BMAA in Surface Waters

The neurotoxin ß-N-methylamino-L-alanine (BMAA) is suspected to play a role in Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis. Because BMAA seems to be produced by cyanobacteria, surface waters are screened for BMAA. However, reliable analysis of BMAA requires specialized and expensive equipment. In 2012, a commercial enzyme-linked immunosorbent assay (ELISA) for determination of BMAA in surface waters was released. This kit could enable fast and relatively cheap screening of surface waters for BMAA. The objective of this study was to determine whether the BMAA ELISA kit was suitable for the determination of BMAA concentrations in surface waters. We hypothesised that the recovery of spiked samples was close to 100% and that the results of unspiked sample analysis were comparable between ELISA and liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. However, we found that recovery was higher than 100% in most spiked samples, highest determined recovery was over 400%. Furthermore, the ELISA gave a positive signal for nearly each tested sample while no BMAA could be detected by LC-MS/MS. We therefore conclude that in its current state, the kit is not suitable for screening surface waters for BMAA

Clinical identification of bacteria in human chronic wound infections: Culturing vs. 16S ribosomal DNA sequencing

Background: Chronic wounds affect millions of people and cost billions of dollars in the United States each year. These wounds harbor polymicrobial biofilm communities, which can be difficult to elucidate using culturing methods. Clinical molecular microbiological methods are increasingly being employed to investigate the microbiota of chronic infections, including wounds, as part of standard patient care. However, molecular testing is more sensitive than culturing, which results in markedly different results being reported to clinicians. This study compares the results of aerobic culturing and molecular testing (culture-free 16S ribosomal DNA sequencing), and it examines the relative abundance score that is generated by the molecular test and the usefulness of the relative abundance score in predicting the likelihood that the same organism would be detected by culture.Methods: Parallel samples from 51 chronic wounds were studied using aerobic culturing and 16S DNA sequencing for the identification of bacteria.Results: One hundred forty-five (145) unique genera were identified using molecular methods, and 68 of these genera were aerotolerant. Fourteen (14) unique genera were identified using aerobic culture methods. One-third (31/92) of the cultures were determined to be < 1% of the relative abundance of the wound microbiota using molecular testing. At the genus level, molecular testing identified 85% (78/92) of the bacteria that were identified by culture. Conversely, culturing detected 15.7% (78/497) of the aerotolerant bacteria and detected 54.9% of the collective aerotolerant relative abundance of the samples. Aerotolerant bacterial genera (and individual species including Staphylococcus aureus, Pseudomonas aeruginosa, and Enterococcus faecalis) with higher relative abundance scores were more likely to be detected by culture as demonstrated with regression modeling.Conclusion: Discordance between molecular and culture testing is often observed. However, culture-free 16S ribosomal DNA sequencing and its relative abundance score can provide clinicians with insight into which bacteria are most abundant in a sample and which are most likely to be detected by culture. © 2012 Rhoads et al.; licensee BioMed Central Ltd

The preferences of 600 patients for different descriptions of randomisation

A total of 600 patients from cancer centres throughout the UK identified their most preferred and most disliked descriptions of randomisation found in current patient information sheets and websites. The CancerBACUP description, which describes both the process of randomisation and why it is done, was most preferred 151 out of 533 (28%) patients. The NCI description was viewed as overly technical and most disliked 185 out of 483 (38%) patients

Simulated responses of soil carbon to climate change in CMIP6 Earth system models: the role of false priming

This is the final version. Available from Copernicus Publications / European Geosciences Union via the DOI in this record. The CMIP data analysed during this study are available online: CMIP6 (https://esgf-node.llnl.gov/search/cmip6/, last access: 8 April 2022) and CMIP5 (https://esgf-node.llnl.gov/search/cmip5/, last access: 12 April 2022).Code is available on GitHub (https://github.com/rebeccamayvarney/CMIP6_dCs, last access: 28 July 2023).Reliable estimates of soil carbon change are required to determine the carbon budgets consistent with the Paris Agreement climate targets. This study evaluates projections of soil carbon during the 21st century in Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models (ESMs) under a range of atmospheric composition scenarios. In general, we find a reduced spread of changes in global soil carbon (ΔCs) in CMIP6 compared to the previous CMIP5 model generation. However, similar reductions were not seen in the derived contributions to ΔCs due to both increases in plant net primary productivity (NPP, named ΔCs,NPP) and reductions in the effective soil carbon turnover time (τs, named ΔCs,τ). Instead, we find a strong relationship across the CMIP6 models between these NPP and τs components of ΔCs, with more positive values of ΔCs,NPP being correlated with more negative values of ΔCs,τ. We show that the concept of “false priming” is likely to be contributing to this emergent relationship, which leads to a decrease in the effective soil carbon turnover time as a direct result of NPP increase and occurs when the rate of increase in NPP is relatively fast compared to the slower timescales of a multi-pool soil carbon model. This finding suggests that the structure of soil carbon models within ESMs in CMIP6 has likely contributed towards the reduction in the overall model spread in future soil carbon projections since CMIP5.European Union’s Horizon 2020European Union’s Horizon 202

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

Elevated CO₂ does not increase eucalypt forest productivity on a low-phosphorus soil

Rising atmospheric CO2 stimulates photosynthesis and productivity of forests, offsetting CO2 emissions. Elevated CO2 experiments in temperate planted forests yielded ~23% increases in productivity over the initial years. Whether similar CO2 stimulation occurs in mature evergreen broadleaved forests on low-phosphorus (P) soils is unknown, largely due to lack of experimental evidence. This knowledge gap creates major uncertainties in future climate projections as a large part of the tropics is P-limited. Here,we increased atmospheric CO2 concentration in a mature broadleaved evergreen eucalypt forest for three years, in the first large-scale experiment on a P-limited site. We show that tree growth and other aboveground productivity components did not significantly increase in response to elevated CO2 in three years, despite a sustained 19% increase in leaf photosynthesis. Moreover, tree growth in ambient CO2 was strongly P-limited and increased by ~35% with added phosphorus. The findings suggest that P availability may potentially constrain CO2-enhanced productivity in P-limited forests; hence, future atmospheric CO2 trajectories may be higher than predicted by some models. As a result, coupled climate-carbon models should incorporate both nitrogen and phosphorus limitations to vegetation productivity in estimating future carbon sinks

A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming (article)

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: The datasets analysed during this study are available online: CMIP5 model output [https://esgf-node.llnl.gov/search/CMIP5/], CMIP6 model output [https://esgf-node.llnl.gov/search/cmip6/], The WFDEI Meteorological Forcing Data [https://rda.ucar.edu/datasets/ds314.2/], CARDAMOM Heterotrophic Respiration [https://datashare.is.ed.ac.uk/handle/10283/875], MODIS Net Primary Production [https://lpdaac.usgs.gov/products/mod17a3v055/], Raich et al. 2002 Soil Respiration [https://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp081/ndp081.html], Hashimoto et al. 2015 Heterotrophic Respiration [http://cse.ffpri.affrc.go.jp/shojih/data/index.html], and the datasets for observational Soil Carbon [https://github.com/rebeccamayvarney/soiltau_ec].Code availability: The Python code used to complete the analysis and produce the figures in this study is available in the following online repository [https://github.com/rebeccamayvarney/soiltau_ec].Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τs) with warming. An approximation to the latter term for the top one metre of soil (ΔCs,τ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (-196 ± 117 PgC). Here, we present a constraint on ΔCs,τ, which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔCs,τ at 2 °C to -232 ± 52 PgC