University of Bremen

About: University of Bremen is a education organization based out in Bremen, Germany. It is known for research contribution in the topics: Population & Context (language use). The organization has 14563 authors who have published 37279 publications receiving 970381 citations. The organization is also known as: Universität Bremen.

Gaia Data Release 1 Summary of the astrometric, photometric, and survey properties

Abstract: Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims: A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods: The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results: Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the Hipparcos and Tycho-2 catalogues - a realisation of the Tycho-Gaia Astrometric Solution (TGAS) - and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of 3000 Cepheid and RR Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr-1 for the proper motions. A systematic component of 0.3 mas should be added to the parallax uncertainties. For the subset of 94 000 Hipparcos stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr-1. For the secondary astrometric data set, the typical uncertainty of the positions is 10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to0.03 mag over the magnitude range 5 to 20.7. Conclusions: Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data.

Defining the core Arabidopsis thaliana root microbiome

Abstract: Sequencing of the Arabidopsis thaliana root microbiome shows that its composition is strongly influenced by location, inside or outside the root, and by soil type. The association between a land plant and the soil microbes of the root microbiome is important for the plant's well-being. A deeper understanding of these microbial communities will offer opportunities to control plant growth and susceptibility to pathogens, particularly in sustainable agricultural regimes. Two groups, working separately but developing best-practice protocols in parallel, have characterized the root microbiota of the model plant Arabidopis thaliana. Working on two continents and with five different soil types, they reach similar general conclusions. The bacterial communities in each root compartment — the rhizosphere immediately surrounding the root and the endophytic compartment within the root — are most strongly influenced by soil type, and to a lesser degree by host genotype. In natural soils, Arabidopsis plants are preferentially colonized by Actinobacteria, Proteobacteria, Bacteroidetes and Chloroflexi species. And — an important point for future work — Arabidopsis root selectivity for soil bacteria under controlled environmental conditions mimics that of plants grown in a natural environment. Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation1,2,3. Colonization of the root occurs despite a sophisticated plant immune system4,5, suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We describe different bacterial communities in two geochemically distinct bulk soils and in rhizosphere and endophytic compartments prepared from roots grown in these soils. The communities in each compartment are strongly influenced by soil type. Endophytic compartments from both soils feature overlapping, low-complexity communities that are markedly enriched in Actinobacteria and specific families from other phyla, notably Proteobacteria. Some bacteria vary quantitatively between plants of different developmental stage and genotype. Our rigorous definition of an endophytic compartment microbiome should facilitate controlled dissection of plant–microbe interactions derived from complex soil communities.

Southward Migration of the Intertropical Convergence Zone Through the Holocene

Abstract: Titanium and iron concentration data from the anoxic Cariaco Basin, off the Venezuelan coast, can be used to infer variations in the hydrological cycle over northern South America during the past 14,000 years with subdecadal resolution. Following a dry Younger Dryas, a period of increased precipitation and riverine discharge occurred during the Holocene “thermal maximum.” Since ∼5400 years ago, a trend toward drier conditions is evident from the data, with high-amplitude fluctuations and precipitation minima during the time interval 3800 to 2800 years ago and during the “Little Ice Age.” These regional changes in precipitation are best explained by shifts in the mean latitude of the Atlantic Intertropical Convergence Zone (ITCZ), potentially driven by Pacific-based climate variability. The Cariaco Basin record exhibits strong correlations with climate records from distant regions, including the high-latitude Northern Hemisphere, providing evidence for global teleconnections among regional climates.

Statistical mechanics of community detection.

Abstract: Starting from a general ansatz, we show how community detection can be interpreted as finding the ground state of an infinite range spin glass. Our approach applies to weighted and directed networks alike. It contains the ad hoc introduced quality function from [J. Reichardt and S. Bornholdt, Phys. Rev. Lett. 93, 218701 (2004)] and the modularity Q as defined by Newman and Girvan [Phys. Rev. E 69, 026113 (2004)] as special cases. The community structure of the network is interpreted as the spin configuration that minimizes the energy of the spin glass with the spin states being the community indices. We elucidate the properties of the ground state configuration to give a concise definition of communities as cohesive subgroups in networks that is adaptive to the specific class of network under study. Further, we show how hierarchies and overlap in the community structure can be detected. Computationally efficient local update rules for optimization procedures to find the ground state are given. We show how the ansatz may be used to discover the community around a given node without detecting all communities in the full network and we give benchmarks for the performance of this extension. Finally, we give expectation values for the modularity of random graphs, which can be used in the assessment of statistical significance of community structure.

Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota

Abstract: The plant root defines the interface between a multicellular eukaryote and soil, one of the richest microbial ecosystems on Earth. Notably, soil bacteria are able to multiply inside roots as benign endophytes and modulate plant growth and development, with implications ranging from enhanced crop productivity to phytoremediation. Endophytic colonization represents an apparent paradox of plant innate immunity because plant cells can detect an array of microbe-associated molecular patterns (also known as MAMPs) to initiate immune responses to terminate microbial multiplication. Several studies attempted to describe the structure of bacterial root endophytes; however, different sampling protocols and low-resolution profiling methods make it difficult to infer general principles. Here we describe methodology to characterize and compare soil- and root-inhabiting bacterial communities, which reveals not only a function for metabolically active plant cells but also for inert cell-wall features in the selection of soil bacteria for host colonization. We show that the roots of Arabidopsis thaliana, grown in different natural soils under controlled environmental conditions, are preferentially colonized by Proteobacteria, Bacteroidetes and Actinobacteria, and each bacterial phylum is represented by a dominating class or family. Soil type defines the composition of root-inhabiting bacterial communities and host genotype determines their ribotype profiles to a limited extent. The identification of soil-type-specific members within the root-inhabiting assemblies supports our conclusion that these represent soil-derived root endophytes. Surprisingly, plant cell-wall features of other tested plant species seem to provide a sufficient cue for the assembly of approximately 40% of the Arabidopsis bacterial root-inhabiting microbiota, with a bias for Betaproteobacteria. Thus, this root sub-community may not be Arabidopsis-specific but saprophytic bacteria that would naturally be found on any plant root or plant debris in the tested soils. By contrast, colonization of Arabidopsis roots by members of the Actinobacteria depends on other cues from metabolically active host cells.