Analysis of resonance multipoles from polarization observables in eta photoproduction

A combined analysis of new eta photoproduction data for total and differential cross sections, target asymmetry and photon asymmetry is presented. Using a few reasonable assumptions we perform the first model-independent analysis of the E0+, E2- and M2- eta photoproduction multipoles. Making use of the well-known A3/2 helicity amplitude of the D13(1520) state we extract its branching ratio to the eta-N channel, Gamma(eta,N)/Gamma = (0.08 +- 0.01)%. At higher energies, we show that the photon asymmetry is extremely sensitive to small multipoles that are excited by photons in the helicity 3/2 state. The new GRAAL photon asymmetry data at higher energy show a clear signal of the F15(1680) excitation which permits extracting an F15(1680)->eta,N branching ratio of (0.15 +0.35 -0.10)%.Comment: 14 pages of LATEX including 7 postscript figure

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Comparison of Bacterial Diversity within the Coral Reef Sponge, Axinella corrugata, and the Encrusting Coral Erythropodium caribaeorum

We compared the Caribbean reef sponge, Axinella corrugata, with the Caribbean reef coral, Erythropodium caribaeorum for differences in their resident microbial communities. This cursory survey of bacterial diversity applied 16S rRNA gene sequences. Over 100 culture-independent sequences were generated from five different Axinella 16S rRNA libraries, and compared with 69 cultured isolates. The cultureindependent 16S rDNA clones displayed a higher diversity of Proteobacteria, including “uncultured” or “unknown” representatives from the Deltaproteobacteria. Arcobacterium, and Cyanobacteria were also found. We have also confirmed that Axinella sponges appeared to host specific microbial symbionts, similar to the previously identified clones termed “OSO” environmental samples. In contrast, seawater samples near Axinella were dominated by Pseudoalteromonas. Adjacent sediment samples yielded clones of Planctomycetacea, Proteobacteria, sulfate-reducing Desulfovibrio spp, and other Deltaproteobacteria. Anaerobe-like 16S rRNA sequences were detected after the oxygen supply to one Axinella sample was deliberately curtailed to assess temporal changes in the microbial community. E. caribaeorum yielded more Betaproteobacteria relative to Axinella 16S libraries, and also included the Gammaproteobacteria genus Spongiobacter. However, Axinella-derived microbes appeared phylogenetically deeper with greater sequence divergences than the coral. Overall this study indicated that marine microbial community diversity can be linked to specific source hosts and habitats

Regional but not global temperature variability underestimated by climate models at supradecadal timescales

Knowledge of the characteristics of natural climate variability is vital when assessing the range of plausible future climate trajectories in the next decades to centuries. The reliable detection of climate fluctuations on multidecadal to centennial timescales depends on proxy reconstructions and model simulations, as the instrumental record extends back only a few decades in most parts of the world. Systematic comparisons between model-simulated and proxy-based inferences of natural variability, however, often seem contradictory. Locally, simulated temperature variability is consistently smaller on multidecadal and longer timescales than is indicated by proxy-based reconstructions, implying that climate models or proxy interpretations might have deficiencies. In contrast, at global scales, studies found agreement between simulated and proxy reconstructed temperature variations. Here we review the evidence regarding the scale of natural temperature variability during recent millennia. We identify systematic reconstruction deficiencies that may contribute to differing local and global model–proxy agreement but conclude that they are probably insufficient to resolve such discrepancies. Instead, we argue that regional climate variations persisted for longer timescales than climate models simulating past climate states are able to reproduce. This would imply an underestimation of the regional variability on multidecadal and longer timescales and would bias climate projections and attribution studies. Thus, efforts are needed to improve the simulation of natural variability in climate models accompanied by further refining proxy-based inferences of variability.This study was undertaken by members of CVAS and 2k Network, working groups of the Past Global Changes (PAGES) Global Research association. This is a contribution to the SPACE ERC, STACY and PALMOD projects. The SPACE ERC project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 716092). STACY has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 395588486). This work has also been supported by the German Federal Ministry of Education and Research (BMBF), through the PalMod project (subprojects 01LP1926B (O.B.), 01LP1926D (M.C.) and 01LP1926C (B.E., P.S. and N.W.)) from the Research for Sustainability initiative (FONA). B.E. is supported by the Heinrich Böll Foundation. E.M.-C. was supported by the PARAMOUR project, funded by the Fonds de la Recherche Scientifique–FNRS and the FWO under the Excellence of Science (EOS) programme (grant no. O0100718F, EOS ID no. 30454083). A.H. was supported by a Legacy Grant from the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage. B.M. was supported by LINKA20102 and the Spanish Ministry of Science and Innovation project CEX2018‐000794‐S. The work originated from discussions at the CVAS working group of PAGES at a workshop at the Internationales Wissenschaftsforum Heidelberg, which was funded by a Hengstberger Prize. We thank N. Beech, C. Brierley, F. Gonzalez-Rouco and M. MacPartland for comments on earlier drafts of the manuscript. This manuscript uses data provided by the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP and PMIP. We thank the research groups for producing and kindly making their model outputs, measurements and palaeoclimate reconstructions available to us. Editorial assistance, in the form of language editing and correction, was provided by XpertScientific Editing and Consulting Services. We acknowledge support by the Open Access Publication Funds of Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung.Peer ReviewedPostprint (author's final draft

PHOTOCHEMICAL RING-OPENING IN meso-CHLORINATED CHLOROPHYLLS

Irradiation of 20-chloro-chlorophylls of the a-type with visible light produces long-wavelength shifted photoproducts, which transform in the dark to linear tetrapyrroles (bile pigments). The possible significance for chlorophyll degradation is discussed

MicroRNA profiling reveals marker of motor neuron disease in ALS models

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder marked by the loss of motor neurons (MNs) in the brain and spinal cord, leading to fatally debilitating weakness. Because this disease predominantly affects MNs, we aimed to characterize the distinct expression profile of that cell type to elucidate underlying disease mechanisms and to identify novel targets that inform on MN health during ALS disease time course. microRNAs (miRNAs) are short, noncoding RNAs that can shape the expression profile of a cell and thus often exhibit cell-type-enriched expression. To determine MN-enriched miRNA expression, we used Cre recombinase-dependent miRNA tagging and affinity purification in mice. By defining thein vivomiRNA expression of MNs, all neurons, astrocytes, and microglia, we then focused on MN-enriched miRNAs via a comparative analysis and found that they may functionally distinguish MNs postnatally from other spinal neurons. Characterizing the levels of the MN-enriched miRNAs in CSF harvested from ALS models of MN disease demonstrated that one miRNA (miR-218) tracked with MN loss and was responsive to an ALS therapy in rodent models. Therefore, we have used cellular expression profiling tools to define the distinct miRNA expression of MNs, which is likely to enrich future studies of MN disease. This approach enabled the development of a novel, drug-responsive marker of MN disease in ALS rodents.SIGNIFICANCE STATEMENTAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease in which motor neurons (MNs) in the brain and spinal cord are selectively lost. To develop tools to aid in our understanding of the distinct expression profiles of MNs and, ultimately, to monitor MN disease progression, we identified small regulatory microRNAs (miRNAs) that were highly enriched or exclusive in MNs. The signal for one of these MN-enriched miRNAs is detectable in spinal tap biofluid from an ALS rat model, where its levels change as disease progresses, suggesting that it may be a clinically useful marker of disease status. Furthermore, rats treated with ALS therapy have restored expression of this MN RNA marker, making it an MN-specific and drug-responsive marker for ALS rodents.</jats:p