A systematic forest survey showing an association of Saccharomyces paradoxus with oak leaf litter.

Although we understand the genetics of the laboratory model yeast Saccharomyces cerevisiae very well, we know little about the natural ecology and environment that shaped its genome. Most isolates of Saccharomyces paradoxus, the wild relative of S. cerevisiae, come from oak trees, but it is not known whether this is because oak is their primary habitat. We surveyed leaf litter in a forest in Northern Germany and found a strong correlation between isolation success of wild Saccharomyces and the proximity of the nearest oak. We compared the four most common tree genera and found Saccharomyces most frequently in oak litter. Interestingly, we show that Saccharomyces is much more abundant in oak leaf litter than on oak bark, suggesting that it grows in litter or soil rather than on the surfaces of oaks themselves. The distribution and abundance of Saccharomyces over the course of a year shows that oak leaf litter provides a stable habitat for the yeast, although there was significant tree-to-tree variation. Taken together, our results suggest that leaf litter rather than tree surfaces provide the better habitat for wild Saccharomyces, with oak being the preferred tree genus. 99.5% of all strains (633/636) isolated were S. paradoxus

Measuring microbial fitness in a field reciprocal transplant experiment

Microbial fitness is easy to measure in the laboratory, but difficult to measure in the field. Laboratory fitness assays make use of controlled conditions and genetically modified organisms, neither of which are available in the field. Among other applications, fitness assays can help researchers detect adaptation to different habitats or locations. We designed a competitive fitness assay to detect adaptation of Saccharomyces paradoxus isolates to the habitat they were isolated from (oak or larch leaf litter). The assay accurately measures relative fitness by tracking genotype frequency changes in the field using digital droplet PCR (DDPCR). We expected locally adapted S. paradoxus strains to increase in frequency over time when growing on the leaf litter type from which they were isolated. The DDPCR assay successfully detected fitness differences among S. paradoxus strains, but did not find a tendency for strains to be adapted to the habitat they were isolated from. Instead, we found that the natural alleles of the hexose transport gene we used to distinguish S. paradoxus strains had significant effects on fitness. The origin of a strain also affected its fitness: strains isolated from oak litter were generally fitter than strains from larch litter. Our results suggest that dispersal limitation and genetic drift shape S. paradoxus populations in the forest more than local selection does, although further research is needed to confirm this. Tracking genotype frequency changes using DDPCR is a practical and accurate microbial fitness assay for natural environments

New methods for unmixing sediment grain size data

Grain size distribution (GSD) data are widely used in Earth sciences and although large data sets are regularly generated, detailed numerical analyses are not routine. Unmixing GSDs into components can help understand sediment provenance and depositional regimes/processes. End-member analysis (EMA), which fits one set of end-members to a given data set, is a powerful way to unmix GSDs into geologically meaningful parts. EMA estimates end-members based on covariability within a data set and can be considered as a nonparametric approach. Available EMA algorithms, however, either produce suboptimal solutions or are time consuming. We introduce unmixing algorithms inspired by hyperspectral image analysis that can be applied to GSD data and which provide an improvement over current techniques. Nonparametric EMA is often unable to identify unimodal grain size subpopulations that correspond to single sediment sources. An alternative approach is single-specimen unmixing (SSU), which unmixes individual GSDs into unimodal parametric distributions (e.g., lognormal). We demonstrate that the inherent nonuniqueness of SSU solutions renders this approach unviable for estimating underlying mixing processes. To overcome this, we develop a new algorithm to perform parametric EMA, whereby an entire data set can be unmixed into unimodal parametric end-members (e.g., Weibull distributions). This makes it easier to identify individual grain size subpopulations in highly mixed data sets. To aid investigators in applying these methods, all of the new algorithms are available in AnalySize, which is GUI software for processing and unmixing grain size data

GRMA: Generalized Range Move Algorithms for the efficient optimization of MRFs

Markov Random Fields (MRF) have become an important tool for many vision applications, and the optimization of MRFs is a problem of fundamental importance. Recently, Veksler and Kumar et al. proposed the range move algorithms, which are some of the most successful optimizers. Instead of considering only two labels as in previous move-making algorithms, they explore a large search space over a range of labels in each iteration, and significantly outperform previous move-making algorithms. However, two problems have greatly limited the applicability of range move algorithms: 1) They are limited in the energy functions they can handle (i.e., only truncated convex functions); 2) They tend to be very slow compared to other move-making algorithms (e.g., �-expansion and ��-swap). In this paper, we propose two generalized range move algorithms (GRMA) for the efficient optimization of MRFs. To address the first problem, we extend the GRMAs to more general energy functions by restricting the chosen labels in each move so that the energy function is submodular on the chosen subset. Furthermore, we provide a feasible sufficient condition for choosing these subsets of labels. To address the second problem, we dynamically obtain the iterative moves by solving set cover problems. This greatly reduces the number of moves during the optimization.We also propose a fast graph construction method for the GRMAs. Experiments show that the GRMAs offer a great speedup over previous range move algorithms, while yielding competitive solutions

Long-term (trophic) purinergic signalling: purinoceptors control cell proliferation, differentiation and death

The purinergic signalling system, which uses purines and pyrimidines as chemical transmitters, and purinoceptors as effectors, is deeply rooted in evolution and development and is a pivotal factor in cell communication. The ATP and its derivatives function as a 'danger signal' in the most primitive forms of life. Purinoceptors are extraordinarily widely distributed in all cell types and tissues and they are involved in the regulation of an even more extraordinary number of biological processes. In addition to fast purinergic signalling in neurotransmission, neuromodulation and secretion, there is long-term (trophic) purinergic signalling involving cell proliferation, differentiation, motility and death in the development and regeneration of most systems of the body. In this article, we focus on the latter in the immune/defence system, in stratified epithelia in visceral organs and skin, embryological development, bone formation and resorption, as well as in cancer. Cell Death and Disease (2010) 1, e9; doi:10.1038/cddis.2009.11; published online 14 January 201

Genomic Expansion of Magnetotactic Bacteria Reveals an Early Common Origin of Magnetotaxis with Lineage-specific Evolution

The origin and evolution of magnetoreception, which in diverse prokaryotes and protozoa is known as magnetotaxis and enables these microorganisms to detect Earth’s magnetic field for orientation and navigation, is not well understood in evolutionary biology. The only known prokaryotes capable of sensing the geomagnetic field are magnetotactic bacteria (MTB), motile microorganisms that biomineralize intracellular, membrane-bounded magnetic single-domain crystals of either magnetite (Fe3O4) or greigite (Fe3S4) called magnetosomes. Magnetosomes are responsible for magnetotaxis in MTB. Here we report the first large-scale metagenomic survey of MTB from both northern and southern hemispheres combined with 28 genomes from uncultivated MTB. These genomes expand greatly the coverage of MTB in the Proteobacteria, Nitrospirae, and Omnitrophica phyla, and provide the first genomic evidence of MTB belonging to the Zetaproteobacteria and “Candidatus Lambdaproteobacteria” classes. The gene content and organization of magnetosome gene clusters, which are physically grouped genes that encode proteins for magnetosome biosynthesis and organization, are more conserved within phylogenetically similar groups than between different taxonomic lineages. Moreover, the phylogenies of core magnetosome proteins form monophyletic clades. Together, these results suggest a common ancient origin of iron-based (Fe3O4 and Fe3S4) magnetotaxis in the domain Bacteria that underwent lineage-specific evolution, shedding new light on the origin and evolution of biomineralization and magnetotaxis, and expanding significantly the phylogenomic representation of MTB

Risk factors associated with default from multi- and extensively drug-resistant tuberculosis treatment, uzbekistan: a retrospective cohort analysis.

The Médecins Sans Frontières project of Uzbekistan has provided multidrug-resistant tuberculosis treatment in the Karakalpakstan region since 2003. Rates of default from treatment have been high, despite psychosocial support, increasing particularly since programme scale-up in 2007. We aimed to determine factors associated with default in multi- and extensively drug-resistant tuberculosis patients who started treatment between 2003 and 2008 and thus had finished approximately 2 years of treatment by the end of 2010

Computational Models for Prediction of Yeast Strain Potential for Winemaking from Phenotypic Profiles

Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40 degrees C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naive Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 mu g/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures.Ines Mendes and Ricardo Franco-Duarte are recipients of a fellowship from the Portuguese Science Foundation, FCT (SFRH/BD/74798/2010, SFRH/BD/48591/2008, respectively) and Joao Drumonde-Neves is recipient of a fellowship from the Azores government (M3.1.2/F/006/2008 (DRCT)). Financial support was obtained from FEDER funds through the program COMPETE and by national funds through FCT by the projects FCOMP-01-0124-008775 (PTDC/AGR-ALI/103392/2008) and PTDC/AGR-ALI/121062/2010. Lan Umek and Blaz Zupan acknowledge financial support from Slovene Research Agency (P2-0209). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Reionization and cosmic dawn astrophysics from the Square Kilometre Array:impact of observing strategies

Interferometry of the cosmic 21-cm signal is set to revolutionize our understanding of the epoch of reionization (EoR) and the cosmic dawn (CD). The culmination of ongoing efforts will be the upcoming Square Kilometre Array (SKA), which will provide tomography of the 21-cm signal from the first billion years of our Universe. Using a galaxy formation model informed by high-z luminosity functions, here we forecast the accuracy with which the first phase of SKA-low (SKA1-low) can constrain the properties of the unseen galaxies driving the astrophysics of the EoR and CD. We consider three observing strategies: (i) deep (1000 h on a single field); (ii) medium-deep (100 h on 10 independent fields); and (iii) shallow (10 h on 100 independent fields). Using the 21-cm power spectrum as a summary statistic, and conservatively only using the 21-cm signal above the foreground wedge, we predict that all three observing strategies should recover astrophysical parameters to a fractional precision of 3c0.1-10 per cent. The reionization history is recovered to an uncertainty of \u394z 7e 0.1 (1\u3c3 ) for the bulk of its duration. The medium-deep strategy, balancing thermal noise against cosmic variance, results in the tightest constraints, slightly outperforming the deep strategy. The shallow observational strategy performs the worst, with up to an 3c10-60 per cent increase in the recovered uncertainty. We note, however, that non-Gaussian summary statistics, tomography, as well as unbiased foreground removal would likely favour the deep strategy