Potential Dependence of Surfactant Adsorption at the Graphite Electrode / Deep Eutectic Solvent Interface

Atomic force microscope (AFM) and cyclic voltammetry (CV) are used to probe how ionic surfactant adsorbed layer structure affects redox processes at deep eutectic solvent (DES)/graphite interfaces. Unlike its behaviour in water, sodium dodecyl sulphate (SDS) in DESs only adsorbs as a complete layer of hemicylindrical hemimicelles far above its critical micelle concentration (CMC). Near the CMC it forms a tail-to-tail monolayer at OCP and positive potentials, and which desorbs at negative potentials. In contrast, cetyltrimethylammonium bromide (CTAB) adsorbs as hemimicelles at low concentrations, and remains adsorbed at both positive and negative potentials. The SDS horizontal monolayer has little overall effect on redox processes at the graphite interface, but hemimicelles form an effective and stable barrier. The stronger solvophobic interactions between the C16 versus C12 alkyl chains in the DES allow CTAB to self-assemble into a robust coating at low concentrations, and illustrate how the structure of the DES/electrode interface and electrochemical response can be engineered by controlling surfactant structure

Irony Detection in Twitter: The Role of Affective Content

© ACM 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, Vol. 16. http://dx.doi.org/10.1145/2930663.[EN] Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazu Hernandez Farias (Grant No. 218109/313683 CVU-369616). The work of Viviana Patti was partially carried out at the Universitat Politecnica de Valencia within the framework of a fellowship of the University of Turin cofunded by Fondazione CRT (World Wide Style Program 2). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030).Hernandez-Farias, DI.; Patti, V.; Rosso, P. (2016). Irony Detection in Twitter: The Role of Affective Content. ACM Transactions on Internet Technology. 16(3):19:1-19:24. https://doi.org/10.1145/2930663S19:119:24163Rob Abbott, Marilyn Walker, Pranav Anand, Jean E. Fox Tree, Robeson Bowmani, and Joseph King. 2011. 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Association for Computational Linguistics, Rochester, NY, 1--4.Florian Kunneman, Christine Liebrecht, Margot van Mulken, and Antal van den Bosch. 2015. Signaling sarcasm: From hyperbole to hashtag. Information Processing & Management 51, 4 (2015), 500--509.Christopher Lee and Albert Katz. 1998. The differential role of ridicule in sarcasm and irony. Metaphor and Symbol 13, 1 (1998), 1--15.John S. Leggitt and Raymond W. Gibbs. 2000. Emotional reactions to verbal irony. Discourse Processes 29, 1 (2000), 1--24.Stephanie Lukin and Marilyn Walker. 2013. Really? Well. Apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue. In Proceedings of the Workshop on Language Analysis in Social Media. Association for Computational Linguistics, Atlanta, GA, 30--40.Diana Maynard and Mark Greenwood. 2014. Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. 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CEUR-WS.org, Heraklion, Crete, Greece, 93--98.W. Gerrod Parrot. 2001. Emotions in Social Psychology: Essential Readings. Psychology Press, Philadelphia, PA.James W. Pennebaker, Martha E. Francis, and Roger J. Booth. 2001. Linguistic Inquiry and Word Count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71.Robert Plutchik. 2001. The nature of emotions. American Scientist 89, 4 (2001), 344--350.Soujanya Poria, Alexander Gelbukh, Amir Hussain, Newton Howard, Dipankar Das, and Sivaji Bandyopadhyay. 2013. Enhanced senticnet with affective labels for concept-based opinion mining. IEEE Intelligent Systems 28, 2 (2013), 31--38.Tomáš Ptáček, Ivan Habernal, and Jun Hong. 2014. Sarcasm detection on Czech and English twitter. In Proceedings of the 25th International Conference on Computational Linguistics (COLING’14). Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 213--223.Ashwin Rajadesingan, Reza Zafarani, and Huan Liu. 2015. 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Impaired “affective theory of mind” is associated with right ventromedial prefrontal damage. Cognitive Behavioral Neurology 18, 1 (2005), 55--67.Philip J. Stone and Earl B. Hunt. 1963. A computer approach to content analysis: Studies using the general inquirer system. In Proceedings of the May 21-23, 1963, Spring Joint Computer Conference (AFIPS’63 (Spring)). ACM, New York, NY, 241--256.Emilio Sulis, Delia Irazú Hernández Farías, Paolo Rosso, Viviana Patti, and Giancarlo Ruffo. 2016. Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems. In Press. Available online.Maite Taboada and Jack Grieve. 2004. Analyzing appraisal automatically. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications. AAAI, Stanford, CA, 158--161.Yi-jie Tang and Hsin-Hsi Chen. 2014. Chinese irony corpus construction and ironic structure analysis. 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Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Crop Updates 2007 - Farming Systems

This session covers forty papers from different authors: 1. Quality Assurance and industry stewardship, David Jeffries, Better Farm IQ Manager, Cooperative Bulk Handling 2. Sothis: Trifolium dasyurum (Eastern Star clover), A. Loi, B.J. Nutt and C.K. Revell, Department of Agriculture and Food 3. Poor performing patches of the paddock – to ameliorate or live with low yield? Yvette Oliver1, Michael Robertson1, Bill Bowden2, Kit Leake3and Ashley Bonser3, CSIRO Sustainable Ecosystems1, Department of Food and Agriculture2, Kellerberrin Farmer3 4. What evidence is there that PA can pay? Michael Robertson, CSIRO Floreat, Ian Maling, SilverFox Solutions and Bindi Isbister, Department of Agriculture and Food 5.The journey is great, but does PA pay? Garren Knell, ConsultAg; Alison Slade, Department of Agriculture and Food, CFIG 6. 2007 Seasonal outlook, David Stephens and Michael Meuleners, Department of Agriculture and Food 7. Towards building farmer capacity to better manage climate risk, David Beard and Nicolyn Short, Department of Agriculture and Food 8. A NAR farmers view of his farming system in 2015, Rob Grima, Department of Agriculture and Food 9. Biofuels opportunities in Australia, Ingrid Richardson, Food and Agribusiness Research, Rabobank 10. The groundwater depth on the hydrological benefits of lucerne and the subsequent recharge values, Ruhi Ferdowsian1and Geoff Bee2; 1Department of Agriculture and Food, 2Landholder, Laurinya, Jerramungup 11. Subsoil constraints to crop production in the high rainfall zone of Western Australia, Daniel Evans1, Bob Gilkes1, Senthold Asseng2and Jim Dixon3; 1University of Western Australia, 2CSIRO Plant Industry, 3Department of Agriculture and Food 12. Prospects for lucerne in the WA wheatbelt, Michael Robertson, CSIRO Floreat, Felicity Byrne and Mike Ewing, CRC for Plant-Based Management of Dryland Salinity, Dennis van Gool, Department of Agriculture and Food 13. Nitrous oxide emissions from a cropped soil in the Western Australian grainbelt, Louise Barton1, Ralf Kiese2, David Gatter3, Klaus Butterbach-Bahl2, Renee Buck1, Christoph Hinz1and Daniel Murphy1,1School of Earth and Geographical Sciences, The University of Western Australia, 2Institute for Meteorology and Climate Research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany, 3The Department of Agriculture and Food 14. Managing seasonal risk is an important part of farm management but is highly complex and therefore needs a ‘horses for courses’ approach, Cameron Weeks, Planfarm / Mingenew-Irwin Group, Dr Michael Robertson, Dr Yvette Oliver, CSIRO Sustainable Ecosystems and Dr Meredith Fairbanks, Department of Agriculture and Food 15. Novel use application of clopyralid in lupins, John Peirce, and Brad Rayner Department of Agriculture and Food 16. Long season wheat on the South Coast – Feed and grain in a dry year – a 2006 case study, Sandy White, Department of Agriculture and Food 17. Wheat yield response to potassium and the residual value of PKS fertiliser drilled at different depths, Paul Damon1, Bill Bowden2, Qifu Ma1 and Zed Rengel1; Faculty of Natural and Agricultural Sciences, The University of Western Australia1, Department of Agriculture and Food2 18. Saltbush as a sponge for summer rain, Ed Barrett-Lennard and Meir Altman, Department of Agriculture and Food and CRC for Plant-based Management of Dryland Salinity 19. Building strong working relationships between grower groups and their industry partners, Tracey M. Gianatti, Grower Group Alliance 20. To graze or not to graze – the question of tactical grazing of cereal crops, Lindsay Bell and Michael Robertson, CSIRO Sustainable Ecosystems 21. Can legume pastures and sheep replace lupins? Ben Webb and Caroline Peek, Department of Agriculture and Food 22. EverGraze – livestock and perennial pasture performance during a drought year, Paul Sanford, Department of Agriculture and Food, and CRC for Plant-based Management of Dryland Salinity 23. Crop survival in challenging times, Paul Blackwell1, Glen Riethmuller1, Darshan Sharma1and Mike Collins21Department of Agriculture and Food, 2Okura Plantations, Kirikiri New Zealand 24. Soil health constraints to production potential – a precision guided project, Frank D’Emden, and David Hall, Department of Agriculture and Food 25. A review of pest and disease occurrence in 2006, Mangano, G.P. and Severtson, D.L., Department of Agriculture and Food 26. e-weed – an information resource on seasonal weed management issues, Vanessa Stewart and Julie Roche, Department of Agriculture and Food 27. Review of Pesticide Legislation and Policies in Western Australia, Peter Rutherford, BSc (Agric.), Pesticide Legislation Review, Office of the Chief Medical Adviser, WA Department of Health 28. Future wheat yields in the West Australian wheatbelt, Imma Farré and Ian Foster, Department of Agriculture and Food, Stephen Charles, CSIRO Land and Water 29. Organic matter in WA arable soils: What’s active and what’s not, Frances Hoyle, Department of Agriculture and Food, Australia and Daniel Murphy, UWA 30. Soil quality indicators in Western Australian farming systems, D.V. Murphy1, N. Milton1, M. Osman1, F.C. Hoyle2, L.K Abbott1, W.R. Cookson1and S. Darmawanto1; 1UWA, 2Department of Agriculture and Food 31. Impact of stubble on input efficiencies, Geoff Anderson, formerly employed by Department of Agriculture and Food 32. Mixed farming vs All crop – true profit, not just gross margins, Rob Sands and David McCarthy, FARMANCO Management Consultants, Western Australia 33. Evaluation of Local Farmer Group Network – group leaders’ surveys 2005 and 2006, Paul Carmody, Local Farmer Group Network, Network Coordinator, UWA 34. Seeding rate and nitrogen application and timing effects in wheat, J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 35. Foliar fungicide application and disease control in barley, J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 36. Brown manuring effects on a following wheat crop in the central wheatbelt, , J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 37. Management of annual pastures in mixed farming systems – transition from a dry season, Dr Clinton Revell and Dr Phil Nichols; Department of Agriculture and Food 38. The value of new annual pastures in mixed farm businesses of the wheatbelt, Dr Clinton Revell1, Mr Andrew Bathgate2and Dr Phil Nichols1; 1Department of Agriculture and Food, 2Farming Systems Analysis Service, Albany 39. The influence of winter SOI and Indian Ocean SST on WA winter rainfall, Meredith Fairbanks and Ian Foster, Department of Agriculture and Food 40. Market outlook – Grains, Anne Wilkins, Market Analyst, Grains, Department of Agriculture and Foo

Multi-messenger Observations of a Binary Neutron Star Merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ∼ 1.7 {{s}} with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of {40}-8+8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 {M}⊙ . An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ∼ 40 {{Mpc}}) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient's position ∼ 9 and ∼ 16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.The AST3 project is supported by the National Basic Research Program (973 Program) of China (Grant Nos. 2013CB834901, 2013CB834900, 2013CB834903), and the Chinese Polar Environment Comprehensive Investigation & Assessment Program (grant No. CHINARE2016-02-03-05). The construction of the AST3 telescopes has received fundings from Tsinghua University, Nanjing University, Beijing Normal University, University of New South Wales, and Texas A&M University, the Australian Antarctic Division, and the National Collaborative Research Infrastructure Strategy (NCRIS) of Australia. It has also received funding from Chinese Academy of Sciences through the Center for Astronomical Mega-Science and National Astronomical Observatory of China (NAOC).The collaboration between LIGO/Virgo and EVN/e-MERLIN is part of a project that has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 653477.B.C., V.C., A.G., and W.S.P. gratefully acknowledge NASA funding through contract NNM13AA43C. M.S.B., R.H., P.J., C.A.M., S.P., R.D.P., M.S., and P.V. gratefully acknowledge NASA funding from cooperative agreement NNM11AA01A. E.B. is supported by an appointment to the NASA Postdoctoral Program at the Goddard Space Flight Center, administered by Universities Space Research Association under contract with NASA. D.K., C.A.W.H., C.M. H., and J.R. gratefully acknowledge NASA funding through the Fermi-GBM project. Support for the German contribution to GBM was provided by the Bundesministerium für Bildung und Forschung (BMBF) via the Deutsches Zentrum für Luft und Raumfahrt (DLR) under contract number 50 QV 0301. A. v.K. was supported by the Bundesministeriums für Wirtschaft und Technologie (BMWi) through DLR grant 50 OG 1101. S. M.B. acknowledges support from Science Foundation Ireland under grant 12/IP/1288.Part of the funding for GROND was generously granted from the Leibniz-Prize to Prof. G. Hasinger (DFG grant HA 1850/28-1). “We acknowledge the excellent help in obtaining GROND data from Angela Hempel, Markus Rabus and Régis Lachaume on La Silla.