Influence of the Tungurahua eruption on the ice core records of Chimborazo, Ecuador

International audienceThe comparison of two shallow ice cores recovered in 1999 and 2000 from the same place on the Chimborazo summit glacier revealed the influence of the coincident Tungurahua volcanic eruption on their stable isotope and chemical records. The surface snow melting and water percolation induced from the ash deposition caused a preferential elution and re-localization of certain ionic species, while the stable isotope records were not affected. Additionally, the comparison of the ionic amount and some selected ion ratios preserved along the ice core column reports under which processes the chemical species are introduced in the snow pack, as snow flake condensation nuclei, by atmospheric scavenging or by dry deposition. This preliminary study is essential for the interpretation of the deep Chimborazo ice core, or for other sites where surrounding volcanic activity influences the glaciochemical records

Polynomial Delay Algorithm for Listing Minimal Edge Dominating sets in Graphs

The Transversal problem, i.e, the enumeration of all the minimal transversals of a hypergraph in output-polynomial time, i.e, in time polynomial in its size and the cumulated size of all its minimal transversals, is a fifty years old open problem, and up to now there are few examples of hypergraph classes where the problem is solved. A minimal dominating set in a graph is a subset of its vertex set that has a non empty intersection with the closed neighborhood of every vertex. It is proved in [M. M. Kant\'e, V. Limouzy, A. Mary, L. Nourine, On the Enumeration of Minimal Dominating Sets and Related Notions, In Revision 2014] that the enumeration of minimal dominating sets in graphs and the enumeration of minimal transversals in hypergraphs are two equivalent problems. Hoping this equivalence can help to get new insights in the Transversal problem, it is natural to look inside graph classes. It is proved independently and with different techniques in [Golovach et al. - ICALP 2013] and [Kant\'e et al. - ISAAC 2012] that minimal edge dominating sets in graphs (i.e, minimal dominating sets in line graphs) can be enumerated in incremental output-polynomial time. We provide the first polynomial delay and polynomial space algorithm that lists all the minimal edge dominating sets in graphs, answering an open problem of [Golovach et al. - ICALP 2013]. Besides the result, we hope the used techniques that are a mix of a modification of the well-known Berge's algorithm and a strong use of the structure of line graphs, are of great interest and could be used to get new output-polynomial time algorithms.Comment: proofs simplified from previous version, 12 pages, 2 figure

Dramatic loss of glacier accumulation area on the Tibetan Plateau revealed by ice core tritium and mercury records

Two ice cores were retrieved from high elevations (~5800 m a.s.l.) at Mt. Nyainqêntanglha and Mt. Geladaindong in the southern and central Tibetan Plateau region. The combined tracer analysis of tritium (3H), 210Pb and mercury, along with other chemical records, provided multiple lines of evidence supporting that the two coring sites had not received net ice accumulation since at least the 1950s and 1980s, respectively. These results implied an annual ice loss rate of more than several hundred millimeter water equivalent over the past 30–60 years. Both mass balance modeling at the sites and in situ data from the nearby glaciers confirmed a continuously negative mass balance (or mass loss) in the region due to dramatic warming in recent decades. Along with a recent report on Naimona\u27nyi Glacier in the Himalayas, the findings suggest that the loss of accumulation area of glacier is a possibility from the southern to central Tibetan Plateau at high elevations, probably up to about 5800 m a.s.l. This mass loss raises concerns over the rapid rate of glacier ice loss and associated changes in surface glacier runoff, water availability, and sea levels

Ground-based and airborne in-situ measurements of the Eyjafjallajökull volcanic aerosol plume in Switzerland in spring 2010

The volcanic aerosol plume resulting from the Eyjafjallajökull eruption in Iceland in April and May 2010 was detected in clear layers above Switzerland during two periods (17–19 April 2010 and 16–19 May 2010). In-situ measurements of the airborne volcanic plume were performed both within ground-based monitoring networks and with a research aircraft up to an altitude of 6000 m a.s.l. The wide range of aerosol and gas phase parameters studied at the high altitude research station Jungfraujoch (3580 m a.s.l.) allowed for an in-depth characterization of the detected volcanic aerosol. Both the data from the Jungfraujoch and the aircraft vertical profiles showed a consistent volcanic ash mode in the aerosol volume size distribution with a mean optical diameter around 3 ± 0.3 μm. These particles were found to have an average chemical composition very similar to the trachyandesite-like composition of rock samples collected near the volcano. Furthermore, chemical processing of volcanic sulfur dioxide into sulfate clearly contributed to the accumulation mode of the aerosol at the Jungfraujoch. The combination of these in-situ data and plume dispersion modeling results showed that a significant portion of the first volcanic aerosol plume reaching Switzerland on 17 April 2010 did not reach the Jungfraujoch directly, but was first dispersed and diluted in the planetary boundary layer. The maximum PM<sub>10</sub> mass concentrations at the Jungfraujoch reached 30 μgm<sup>−3</sup> and 70 μgm<sup>−3</sup> (for 10-min mean values) duri ng the April and May episode, respectively. Even low-altitude monitoring stations registered up to 45 μgm<sup>−3</sup> of volcanic ash related PM<sub>10</sub> (Basel, Northwestern Switzerland, 18/19 April 2010). The flights with the research aircraft on 17 April 2010 showed one order of magnitude higher number concentrations over the northern Swiss plateau compared to the Jungfraujoch, and a mass concentration of 320 (200–520) μgm<sup>−3</sup> on 18 May 2010 over the northwestern Swiss plateau. The presented data significantly contributed to the time-critical assessment of the local ash layer properties during the initial eruption phase. Furthermore, dispersion models benefited from the detailed information on the volcanic aerosol size distribution and its chemical composition

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

Predicting protein functions by relaxation labelling protein interaction network

<p>Abstract</p> <p>Background</p> <p>One of key issues in the post-genomic era is to assign functions to uncharacterized proteins. Since proteins seldom act alone; rather, they must interact with other biomolecular units to execute their functions. Thus, the functions of unknown proteins may be discovered through studying their interactions with proteins having known functions. Although many approaches have been developed for this purpose, one of main limitations in most of these methods is that the dependence among functional terms has not been taken into account.</p> <p>Results</p> <p>We developed a new network-based protein function prediction method which combines the likelihood scores of local classifiers with a relaxation labelling technique. The framework can incorporate the inter-relationship among functional labels into the function prediction procedure and allow us to efficiently discover relevant non-local dependence. We evaluated the performance of the new method with one other representative network-based function prediction method using E. coli protein functional association networks.</p> <p>Conclusion</p> <p>Our results showed that the new method has better prediction performance than the previous method. The better predictive power of our method gives new insights about the importance of the dependence between functional terms in protein functional prediction.</p

An iterative approach of protein function prediction

Background: Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms.Results: In this paper, we propose a new prediction approach that predicts protein functions iteratively. This iterative approach incorporates the dynamic and mutual features of PPI interactions, as well as the local and global semantic influence of protein functions, into the prediction. To guarantee predicting functions iteratively, we propose a new protein similarity from protein functions. We adapt new evaluation metrics to evaluate the prediction quality of our algorithm and other similar algorithms. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions.Conclusions: The iterative approach is more likely to reflect the real biological nature between proteins when predicting functions. A proper definition of protein similarity from protein functions is the key to predicting functions iteratively. The evaluation results demonstrated that in most cases, the iterative approach outperformed non-iterative ones with higher prediction quality in terms of prediction precision, recall and F-value

Mesoscopic organization reveals the constraints governing C. elegans nervous system

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations, such as optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis, we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.Comment: Published version, Minor modifications, 16 pages, 9 figure

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study

<p>Abstract</p> <p>Background</p> <p>Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.</p> <p>Results</p> <p>We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, <it>χ</it>²-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a <it>Saccharomyces cerevisiae </it>proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies.</p> <p>Conclusions</p> <p>Physical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.</p