Accounting for changing temperature patterns increases historical estimates of climate sensitivity

Eight atmospheric general circulation models (AGCMs) are forced with observed historical (1871–2010) monthly sea surface temperature and sea ice variations using the Atmospheric Model Intercomparison Project II data set. The AGCMs therefore have a similar temperature pattern and trend to that of observed historical climate change. The AGCMs simulate a spread in climate feedback similar to that seen in coupled simulations of the response to CO2 quadrupling. However, the feedbacks are robustly more stabilizing and the effective climate sensitivity (EffCS) smaller. This is due to a pattern effect, whereby the pattern of observed historical sea surface temperature change gives rise to more negative cloud and longwave clear‐sky feedbacks. Assuming the patterns of long‐term temperature change simulated by models, and the radiative response to them, are credible; this implies that existing constraints on EffCS from historical energy budget variations give values that are too low and overly constrained, particularly at the upper end. For example, the pattern effect increases the long‐term Otto et al. (2013, https://doi.org/10.1038/ngeo1836) EffCS median and 5–95% confidence interval from 1.9 K (0.9–5.0 K) to 3.2 K (1.5–8.1 K

Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms

International audienceBackground : High-throughput sequencing technologies are lifting major limitations to molecular-based ecological studies of eukaryotic microbial diversity, but analyses of the resulting millions of short sequences remain a major bottleneck for these approaches. Here, we introduce the analytical and statistical framework of sequence similarity networks, increasingly used in evolutionary studies and graph theory, into the field of ecology to analyze novel pyrosequenced V4 small subunit rDNA (SSU-rDNA) sequence data sets in the context of previous studies, including SSU-rDNA Sanger sequence data from cultured ciliates and from previous environmental diversity inventories.Results : Our broadly applicable protocol quantified the progress in the description of genetic diversity of ciliates by environmental SSU-rDNA surveys, detected a fundamental historical bias in the tendency to recover already known groups in these surveys, and revealed substantial amounts of hidden microbial diversity. Moreover, network measures demonstrated that ciliates are not globally dispersed, but are structured by habitat and geographical location at intermediate geographical scale, as observed for bacteria, plants, and animals.Conclusions : Currently available ‘universal’ primers used for local in-depth sequencing surveys provide little hope to exhaust the significantly higher ciliate (and most likely microbial) diversity than previously thought. Network analyses such as presented in this study offer a promising way to guide the design of novel primers and to further explore this vast and structured microbial diversity

Statistical modelling of masked gene regulatory pathway changes across microarray studies of interferon gamma activated macrophages

Interferon gamma (IFN-γ) regulation of macrophages plays an essential role in innate immunity and pathogenicity of viral infections by directing large and small genome-wide changes in the transcriptional program of macrophages. Smaller changes at the transcriptional level are difficult to detect but can have profound biological effects, motivating the hypothesis of this thesis that responses of macrophages to immune activation by IFN-γ include small quantitative changes that are masked by noise but represent meaningful transcriptional systems in pathways against infection. To test this hypothesis, statistical meta-analysis of microarray studies is investigated as a tool to obtain the necessary increase in analysis sensitivity. Three meta-analysis models (Effect size model, Rank Product model, Fisher’s sum of logs) and three further modified versions were applied to a heterogeneous set of four microarray studies on the effect of IFN-γ on murine macrophages. Performance assessments include recovery of known biology and are followed by development of novel biological hypotheses through secondary analysis of meta-analysis outcomes in context of independent biological data sources. A separate network analysis of a microarray time course study investigate s if gene sets with coordinated time-dependent relationships overlap can also identify subtle IFN-γ related transcriptional changes in macrophages that match those identified through meta-analysis. It was found that all meta-analysis models can identify biologically meaningful transcription at enhanced sensitivity levels, with slightly improved performance advantages for a non-parametric model (Rank Product meta-analysis). Meta-analysis yielded consistently regulated genes, hidden in individual microarray studies, related to sterol biosynthesis (Stard3, Pgrmc1, Galnt6, Rab11a, Golga4, Lrp10), implicated in cross-talk between type II and type I interferon or IL-10 signalling (Tbk1, Ikbke, Clic4, Ptpre, Batf), and circadian rhythm (Csnk1e). Further network analysis confirms that meta-analysis findings are highly concentrated in a distinct immune response cluster of co-expressed genes, and also identifies global expression modularisation in IFN-γ treated macrophages, pointing to Trafd1 as a central anti-correlated node topologically linked to interactions with down-regulated sterol biosynthesis pathway members. Outcomes from this thesis suggest that small transcriptional changes in IFN-γ activated macrophages can be detected by enhancing sensitivity through combination of multiple microarray studies. Together with use of bioinformatical resources, independent data sets and network analysis, further validation assigns a potential role for low or variable transcription genes in linking type II interferon signalling to type I and TLR signalling, as well as the sterol metabolic network

The SINS/zC-SINF Survey of z~2 Galaxy Kinematics: The Nature of Dispersion Dominated Galaxies

We analyze the spectra, spatial distributions and kinematics of Ha, [NII] and [SII] emission in a sample of 42, z~2.2 UV/optically selected star forming galaxies (SFGs) from the SINS & zC-SINF surveys, 35 of which were observed in the adaptive optics mode of SINFONI. This is supplemented by kinematic data from 48 z~1-2.5 galaxies from the literature. We find that the kinematic classification of the high-z SFGs as `dispersion dominated' or `rotation dominated' correlates most strongly with their intrinsic sizes. Smaller galaxies are more likely `dispersion-dominated' for two main reasons: 1) The rotation velocity scales linearly with galaxy size but intrinsic velocity dispersion does not depend on size, and as such, their ratio is systematically lower for smaller galaxies, and 2) Beam smearing strongly decreases large-scale velocity gradients and increases observed dispersion much more for galaxies with sizes at or below the resolution. Dispersion dominated SFGs may thus have intrinsic properties similar to `rotation dominated' SFGs, but are primarily more compact, lower mass, less metal enriched and may have higher gas fractions, plausibly because they represent an earlier evolutionary state.Comment: 13 pages, 9 figures, accepted by Ap

Recovering Runtime Structures of Software Systems from Static Source Code

Abstract: While software building blocks and their interdependencies can be recovered from the source code using static fact extraction, behavior and communication paths at runtime are typically gathered from instrumented executions of the system. However, more often than not it is not possible to retrieve data from the running system – either due to a high effort for instrumentation, missing (hardware) infrastructure, or because of advanced communication mechanisms hidden by middleware, frameworks or platforms. In this paper, we present an approach to semiautomatically reconstruct runtime components and connectors using source code analysis, pattern matching, and expert knowledge. We present two applications where we could recover runtime communication paths and component interactions despite the absence of runtime traces.

Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing

International audienceAlthough protists are critical components of marine ecosystems, they are still poorly characterized. Here we analysed the taxonomic diversity of planktonic and benthic protist communities collected in six distant European coastal sites. Environmental deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from three size fractions (pico-, nano- and micro/mesoplankton), as well as from dissolved DNA and surface sediments were used as templates for tag pyrosequencing of the V4 region of the 18S ribosomal DNA. Beta-diversity analyses split the protist community structure into three main clusters: picoplankton-nanoplankton-dissolved DNA, micro/mesoplankton and sediments. Within each cluster, protist communities from the same site and time clustered together, while communities from the same site but different seasons were unrelated. Both DNA and RNA-based surveys provided similar relative abundances for most class-level taxonomic groups. Yet, particular groups were overrepresented in one of the two templates, such as marine alveolates (MALV)-I and MALV-II that were much more abundant in DNA surveys. Overall, the groups displaying the highest relative contribution were Dinophyceae, Diatomea, Ciliophora and Acantharia. Also, well represented were Mamiellophyceae, Cryptomonadales, marine alveolates and marine stramenopiles in the picoplankton, and Monadofilosa and basal Fungi in sediments. Our extensive and systematic sequencing of geographically separated sites provides the most comprehensive molecular description of coastal marine protist diversity to date

Global Trends of Benthic Bacterial Diversity and Community Composition Along Organic Enrichment Gradients of Salmon Farms

The analysis of benthic bacterial community structure has emerged as a powerful alternative to traditional microscopy-based taxonomic approaches to monitor aquaculture disturbance in coastal environments. However, local bacterial diversity and community composition vary with season, biogeographic region, hydrology, sediment texture, and aquafarm-specific parameters. Therefore, without an understanding of the inherent variation contained within community complexes, bacterial diversity surveys conducted at individual farms, countries, or specific seasons may not be able to infer global universal pictures of bacterial community diversity and composition at different degrees of aquaculture disturbance. We have analyzed environmental DNA (eDNA) metabarcodes (V3–V4 region of the hypervariable SSU rRNA gene) of 138 samples of different farms located in different major salmon-producing countries. For these samples, we identified universal bacterial core taxa that indicate high, moderate, and low aquaculture impact, regardless of sampling season, sampled country, seafloor substrate type, or local farming and environmental conditions. We also discuss bacterial taxon groups that are specific for individual local conditions. We then link the metabolic properties of the identified bacterial taxon groups to benthic processes, which provides a better understanding of universal benthic ecosystem function(ing) of coastal aquaculture sites. Our results may further guide the continuing development of a practical and generic bacterial eDNA-based environmental monitoring approach.publishedVersio

Variations in antimicrobial resistance genes present in the rectal faeces of seals in Scottish and Liverpool Bay coastal waters

Funding: The work was supported by the Scottish Government Rural and Environment Science and Analytical Services (RESAS) through the Strategic Research Programme 2016–2022. JLB’s PhD studentship was funded by the Moredun Research Institute and the Royal Zoological Society of Scotland.Antibiotic resistance genes originating from human activity are considered important environmental pollutants. Wildlife species can act as sentinels for coastal environmental contamination and in this study we used qPCR array technology to investigate the variety and abundance of antimicrobial resistance genes (ARGs), mobile genetic elements (MGEs) and integrons circulating within seal populations both near to and far from large human populations located around the Scottish and northwest English coast. Rectal swabs were taken from 50 live grey seals and nine live harbour seals. Nucleic acids were stabilised upon collection, enabling extraction of sufficient quality and quantity DNA for downstream analysis. 78 ARG targets, including genes of clinical significance, four MGE targets and three integron targets were used to monitor genes within 22 sample pools. 30 ARGs were detected, as well as the integrons intl1 and intl2 and tnpA transposase. Four β-lactam, nine tetracycline, two phenicol, one trimethoprim, three aminoglycoside and ten multidrug resistance genes were detected as well as mcr-1 which confers resistance to colistin, an important drug of last resort. No sulphonamide, vancomycin, macrolide, lincosamide or streptogramin B (MLSB) resistance genes were detected. Resistance genes were detected in all sites but the highest number of ARGs (n = 29) was detected in samples derived from grey seals on the Isle of May, Scotland during the breeding season, and these genes also had the highest average abundance in relation to the 16S rRNA gene. This pilot study demonstrates the effectiveness of a culture-independent workflow for global analysis of ARGs within the microbiota of live, free-ranging, wild animals from habitats close to and remote from human habitation, and highlights seals as a valuable indicator species for monitoring the presence, abundance and land-sea transference of resistance genes within and between ecosystems.Peer reviewe

Optimization of a parallel permutation testing function for the SPRINT R package

The statistical language R and Bioconductor package arefavoured by many biostatisticians for processing microarraydata. The amount of data produced by these analyses hasreached the limits of many common bioinformatics computinginfrastructures. High Performance Computing (HPC)systems offer a solution to this issue. The Simple Parallel RINTerface (SPRINT) is a package that provides biostatisticianswith easy access to HPC systems and allows the additionof parallelized functions to R. This paper will presenthow we added a parallelized permutation testing functionin R using SPRINT and how this function performs on asupercomputer for executions of up to 512 processes