389 research outputs found

    Automatic design of basin-specific drought indexes for highly regulated water systems

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    [EN] Socio-economic costs of drought are progressively increasing worldwide due to undergoing alterations of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, traditional drought indexes often fail at detecting critical events in highly regulated systems, where natural water availability is conditioned by the operation of water infrastructures such as dams, diversions, and pumping wells. Here, ad hoc index formulations are usually adopted based on empirical combinations of several, supposed-to-be significant, hydro-meteorological variables. These customized formulations, however, while effective in the design basin, can hardly be generalized and transferred to different contexts. In this study, we contribute FRIDA (FRamework for Index-based Drought Analysis), a novel framework for the automatic design of basin-customized drought indexes. In contrast to ad hoc empirical approaches, FRIDA is fully automated, generalizable, and portable across different basins. FRIDA builds an index representing a surrogate of the drought conditions of the basin, computed by combining all the relevant available information about the water circulating in the system identified by means of a feature extraction algorithm. We used the Wrapper for Quasi-Equally Informative Subset Selection (W-QEISS), which features a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables, and optimizing relevance and redundancy of the subset. The preferred variable subset is selected among the efficient solutions and used to formulate the final index according to alternative model structures. We apply FRIDA to the case study of the Jucar river basin (Spain), a drought-prone and highly regulated Mediterranean water resource system, where an advanced drought management plan relying on the formulation of an ad hoc "state index" is used for triggering drought management measures. The state index was constructed empirically with a trial-and-error process begun in the 1980s and finalized in 2007, guided by the experts from the Confederacion Hidrografica del Jucar (CHJ). Our results show that the automated variable selection outcomes align with CHJ's 25-year-long empirical refinement. In addition, the resultant FRIDA index outperforms the official State Index in terms of accuracy in reproducing the target variable and cardinality of the selected inputs set.The work has been partially funded by the European Commission under the IMPREX project belonging to Horizon 2020 framework programme (grant no. 641811). The authors would like to thank the planning office of the Confederacion Hidrografica del Jucar (CHJ) for providing the data used in this study.Zaniolo, M.; Giuliani, M.; Castelletti, A.; Pulido-Velazquez, M. (2018). Automatic design of basin-specific drought indexes for highly regulated water systems. HYDROLOGY AND EARTH SYSTEM SCIENCES. 22(4):2409-2424. https://doi.org/10.5194/hess-22-2409-2018S24092424224AghaKouchak, A.: Recognize anthropogenic drought, Nature, 524, p. 409, 2015a. aAghaKouchak, A.: A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought, J. Hydrol., 526, 127–135, 2015b. aAlcamo, J., Flörke, M., and MĂ€rker, M.: Future long-term changes in global water resources driven by socio-economic and climatic changes, Hydrolog. Sci. J., 52, 247–275, 2007. aAndreu, J., Capilla, J., and SanchĂ­s, E.: AQUATOOL, a generalized decision-support system for water-resources planning and operational management, J. Hydrol., 177, 269–291, 1996. aAndreu, J., Ferrer-Polo, J., PĂ©rez, M., and Solera, A.: Decision support system for drought planning and management in the Jucar river basin, Spain, in: 18th World IMACS/MODSIM Congress, Cairns, Australia, vol. 1317, 2009. a, bBowden, G. J., Dandy, G. C., and Maier, H. R.: Input determination for neural network models in water resources applications. Part 1 – Background and methodology, J. Hydrol., 301, 75–92, https://doi.org/10.1016/j.jhydrol.2004.06.021, 2005. aByun, H.-R. and Wilhite, D. A.: Objective quantification of drought severity and duration, J. Climate, 12, 2747–2756, 1999. aCarmona, M., Måñez Costa, M., Andreu, J., Pulido-Velazquez, M., Haro-Monteagudo, D., Lopez-Nicolas, A., and Cremades, R.: Assessing the effectiveness of Multi-Sector Partnerships to manage droughts: The case of the Jucar river basin, Earth's Future, 5, 750–770, https://doi.org/10.1002/2017EF000545, 2017. a, b, c, dChangnon, S. A.: Detecting drought conditions in Illinois, Circular (Illinois State Water Survey), 1–36, Illinois, USA, 1987. aCHD: Plan Especial de ActuaciĂłn en situaciones de alerta y eventual sequĂ­a, Plan Especial de ActuaciĂłn en situaciones de alerta y eventual sequĂ­a en la cuenca del Duero, TYPSA, Valladolid, 2007. aCHE: Plan especial de actuaciĂłn en situaciones de alerta y eventual sequia en la cuenca hidrogrĂĄfica del Ebro, MARM, Zaragoza, 2007. aCHG: Plan especial de actuaciĂłn en situaciones de alerta y eventual sequĂ­a de la cuenca hidrogrĂĄfica del Guadalquivir, CHG, Seville, Spain, 2007. aCHJ: Plan especial de alerta y eventual sequĂ­a en la confederaciĂłn hidrogrĂĄfica del JĂșcar, ConfederaciĂłn HidrogrĂĄfica del JĂșcar, Jucar River Basin Management Authority, Ministry of Agriculture, Food and Environment, Spanish Government, Valencia, Spain, 2007a (in Spanish). a, b, c, dCHJ: Anejo2 – Plan especial de alerta y eventual sequĂ­a en la confederaciĂłn hidrogrĂĄfica del JĂșcar, ConfederaciĂłn HidrogrĂĄfica del JĂșcar, Jucar River Basin Management Authority, Ministry of Agriculture, Food and Environment, Spanish Government, Valencia, Spain, 2007b (in Spanish). aCunningham, P.: Dimension reduction, in: Machine learning techniques for multimedia, 91–112, Springer, Cognitive Technologies, Springer, Berlin, Heidelberg, 2008. aDracup, J. A., Lee, K. S., and Paulson, E. G.: On the definition of droughts, Water Resour. Res., 16, 297–302, https://doi.org/10.1029/WR016i002p00297, 1980. aEstrela, T. and Vargas, E.: Drought management plans in the European Union. The case of Spain, Water Resour. Manag., 26, 1537–1553, 2012. a, bEU: Water Scarcity and Droughts, Second Interim Report, Tech. rep., 2007. aFalkenmark, M., Lundqvist, J., and Widstrand, C.: Macro-scale water scarcity requires micro-scale approaches, in: Natural resources forum, vol. 13, 258–267, Wiley Online Library, Blackwell Publishing Ltd, 1989. aGalelli, S. and Castelletti, A.: Tree-based iterative input variable selection for hydrological modeling, Water Resour. Res., 49, 4295–4310, 2013. aGalelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., and Gibbs, M. S.: An evaluation framework for input variable selection algorithms for environmental data-driven models, Environ. Modell. Softw., 62, 33–51, https://doi.org/10.1016/j.envsoft.2014.08.015, 2014. aGarrote, L., Martin-Carrasco, F., Flores-Montoya, F., and Iglesias, A.: Linking drought indicators to policy actions in the Tagus basin drought management plan, Water Resour. Manag., 21, 873–882, 2007. a, bGiorgi, F. and Lionello, P.: Climate change projections for the Mediterranean region, Global Planet. Change, 63, 90–104, 2008. aGĂłmez, C. M. G. and Blanco, C. D. P.: Do drought management plans reduce drought risk? A risk assessment model for a Mediterranean river basin, Ecol. Econ., 76, 42–48, 2012. aGustard, A. and Demuth, S.: Operational Hydrology Report No. 50 German National Committee for the International Hydrological Programme (IHP) of UNESCO and the Hydrology and Water Resources Programme (HWRP) of WMO, Koblenz, 2009. aGuyon, I.: An Introduction to Variable and Feature Selection, J. Mach. Learn. Res., 3, 1157–1182, 2003. aHadka, D. and Reed, P.: Borg: An auto-adaptive many-objective evolutionary computing framework, Evol. Comput., 21, 231–259, 2013. aHao, Z. and AghaKouchak, A.: Multivariate standardized drought index: a parametric multi-index model, Adv. Water Resour., 57, 12–18, 2013. aHaro, D., Solera, A., Paredes, J., and Andreu, J.: Methodology for drought risk assessment in within-year regulated reservoir systems. application to the orbigo river system (Spain), Water Resour. Manag., 28, 3801–3814, 2014a. a, bHaro, D., Solera, A., Pedro-MonzonĂ­s, M., and Andreu, J.: Optimal Management of the Jucar River and Turia River Basins under Uncertain Drought Conditions, Procedia Engineer., 89, 1260–1267, 2014b. a, bHaro-Monteagudo, D., Solera, A., and Andreu, J.: Drought early warning based on optimal risk forecasts in regulated river systems: Application to the Jucar River Basin (Spain), J. Hydrol., 544, 36–45, 2017. a, b, cHeim Jr., R. R.: A review of twentieth-century drought indices used in the United States, B. Am. Meteorol. Soc., 83, 1149–1165, 2002. a, b, cHuang, G.-B., Zhu, Q.-Y., and Siew, C.-K.: Extreme learning machine: theory and applications, Neurocomputing, 70, 489–501, 2006. a, bHuang, G.-B., Zhou, H., Ding, X., and Zhang, R.: Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42, 513–529, 2012. aKarakaya, G., Galelli, S., Ahipasaoglu, S. D., and Taormina, R.: Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems for Classification: A Max-Relevance Min-Redundancy Approach, IEEE Transactions on Cybernetics, PP, 1, https://doi.org/10.1109/TCYB.2015.2444435, 2015. a, b, c, d, eKeyantash, J. and Dracup, J. A.: The quantification of drought: an evaluation of drought indices, B. Am. Meteorol. Soc., 83, 1167–1180, 2002. aKeyantash, J. A. and Dracup, J. A.: An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage, Water Resour. Res., 40, 1–13, 2004. aKummu, M., Ward, P. J., de Moel, H., and Varis, O.: Is physical water scarcity a new phenomenon? Global assessment of water shortage over the last two millennia, Environ. Res. Lett., 5, 034006, 2010. aLaaha, G., Gauster, T., Tallaksen, L. M., Vidal, J.-P., Stahl, K., Prudhomme, C., Heudorfer, B., Vlnas, R., Ionita, M., Van Lanen, H. A. J., Adler, M.-J., Caillouet, L., Delus, C., Fendekova, M., Gailliez, S., Hannaford, J., Kingston, D., Van Loon, A. F., Mediero, L., Osuch, M., Romanowicz, R., Sauquet, E., Stagge, J. H., and Wong, W. K.: The European 2015 drought from a hydrological perspective, Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, 2017. aLorenzo-Lacruz, J., Vicente-Serrano, S. M., LĂłpez-Moreno, J. I., BeguerĂ­a, S., GarcĂ­a-Ruiz, J. M., and Cuadrat, J. M.: The impact of droughts and water management on various hydrological systems in the headwaters of the Tagus River (central Spain), J. Hydrol., 386, 13–26, https://doi.org/10.1016/j.jhydrol.2010.01.001, 2010. aMacian-Sorribes, H. and Pulido-Velazquez, M.: Integrating Historical Operating Decisions and Expert Criteria into a DSS for the Management of a Multireservoir System, J. Water Res. Pl., 143, 04016069, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000712, 2017. aMacKay, D. J.: Information theory, inference and learning algorithms, Cambridge university press, 2003. aMarcos-Garcia, P., Lopez-Nicolas, A., and Pulido-Velazquez, M.: Combined use of relative drought indices to analyze climate change impact on meteorological and hydrological droughts in a Mediterranean basin, J. Hydrol., 554, 292–305, 2017. aMcKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, 179–183, American Meteorological Society Boston, MA, 1993. a, bMinisterio del Medio Ambiente: Plan HidrolĂłgico Nacional, â‰ȘBOE≫ nĂșm. 161, de 6 de julio de 2001, 24228–24250, Madrid, Espana, 2000. aMishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010. a, b, c, dNarasimhan, B. and Srinivasan, R.: Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring, Agr. Forest Meteorol., 133, 69–88, https://doi.org/10.1016/j.agrformet.2005.07.012, 2005. aOki, T. and Kanae, S.: Global hydrological cycles and world water resources, Science, 313, 1068–1072, 2006. aPalmer, W. C.: Meteorological drought, vol. 30, US Department of Commerce, Weather Bureau Washington, DC, 1965. aPedro-MonzonĂ­s, M., Ferrer, J., Solera, A., Estrela, T., and Paredes-Arquiola, J.: Water Accounts and Water Stress Indexes in the European Context of Water Planning: the Jucar River Basin, Procedia Engineer., 89, 1470–1477, 2014. aPedro-MonzonĂŹs, M., Solera, A., Ferrer, J., Estrela, T., and Paredes-Arquiola, J.: A review of water scarcity and drought indexes in water resources planning and management, J. Hydrol., 527, 482–493, https://doi.org/10.1016/j.jhydrol.2015.05.003, 2015. a, b, c, dRaskin, P., Gleick, P., Kirshen, P., Pontius, G., and Strzepek, K.: Water futures: Assessment of long-range patterns and problems, Comprehensive assessment of the freshwater resources of the world, SEI, 1997. aReed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., and Kollat, J. B.: Evolutionary multiobjective optimization in water resources: The past, present, and future, Adv. Water Resour., 51, 438–456, 2013. aRijsberman, F. R.: Water scarcity: fact or fiction?, Agr. Water Manage., 80, 5–22, 2006. a, bScott, D. W.: Multivariate density estimation and visualization, in: Handbook of Computational Statistics, edited by: Gentle, J., HĂ€rdle, W., and Mori Y., Springer Handbooks of Computational Statistics, Springer, Berlin, Heidelberg, 549–569, Springer, 2012. aShafer, B. and Dezman, L.: Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas, in: Proceedings of the western snow conference, vol. 50, pp. 164–175, Colorado State University Fort Collins, CO, 1982. aSharma, A.: Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 – A strategy for system predictor identification, J. Hydrol., 239, 232–239, 2000. aSharma, A. and Mehrotra, R.: An information theoretic alternative to model a natural system using observational information alone, Water Resour. Res., 50, 650–660, 2014. a, bSpinoni, J., Naumann, G., Vogt, J., and Barbosa, P.: Meteorological Droughts in Europe, Publications Office of the European Union, ISBN-13: 978-92-79-55097-3, 2016. a, b, c, d, eStahl, K., Kohn, I., Blauhut, V., Urquijo, J., De Stefano, L., AcĂĄcio, V., Dias, S., Stagge, J. H., Tallaksen, L. M., Kampragou, E., Van Loon, A. F., Barker, L. J., Melsen, L. A., Bifulco, C., Musolino, D., de Carli, A., Massarutto, A., Assimacopoulos, D., and Van Lanen, H. A. J.: Impacts of European drought events: insights from an international database of text-based reports, Nat. Hazards Earth Syst. Sci., 16, 801–819, https://doi.org/10.5194/nhess-16-801-2016, 2016. aStaudinger, M., Stahl, K., and Seibert, J.: A drought index accounting for snow, J. Hydrol., 6, 2108–2123, https://doi.org/10.1002/2012WR013085, 2014. aSullivan, C. A., Meigh, J. R., and Giacomello, A. M.: The water poverty index: development and application at the community scale, in: Natural Resources Forum, vol. 27, 189–199, Wiley Online Library, 2003. aTallaksen, L. M. and Van Lanen, H. A.: Hydrological drought: processes and estimation methods for streamflow and groundwater, vol. 48, Elsevier, Amsterdam, NL, 2004. aTaormina, R., Galelli, S., Karakaya, G., and Ahipasaoglu, S.: An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models, J. Hydrol., 542, 18–34, 2016. a, b, cVan Loon, A. F. and Van Lanen, H. A. J.: A process-based typology of hydrological drought, Hydrol. Earth Syst. Sci., 16, 1915–1946, https://doi.org/10.5194/hess-16-1915-2012, 2012. a, bVan Loon, A. F. and Van Lanen, H. A. J.: Making the distinction between water scarcity and drought using an observation-modeling framework, Water Resour. Res., 49, 1483–1502, https://doi.org/10.1002/wrcr.20147, 2013. aVicente-Serrano, S. M. and LĂłpez-Moreno, J. I.: Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin, Hydrol. Earth Syst. Sci., 9, 523–533, https://doi.org/10.5194/hess-9-523-2005, 2005.  aVicente-Serrano, S. M., BeguerĂ­a, S., and LĂłpez-Moreno, J. I.: A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index, J. Climate, 23, 1696–1718, 2010. a, bWanders, N., Van Lanen, H. A., and van Loon, A. F.: Indicators for drought characterization on a global scale, Tech. rep., Wageningen Universiteit, 2010. aWitten, I. H. and Frank, E.: Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann, Cambridge, USA, 2005. aYang, H., Reichert, P., Abbaspour, K. C., and Zehnder, A. J.: A water resources threshold and its implications for food security, Environ. Sci. Technol., 37, 3048–3054, 2003. aYang, Y. and Pedersen, J. O.: A comparative study on feature selection in text categorization, International Conference of Machine Learning ICML, 97, 412–420, 1997. aZaniolo, M., Giuliani, M., Castelletti, A., and Pulido-VelĂ zquez, M.: Raw and processed hydro-meteorological variables of Jucar river basin for feature selection, https://doi.org/10.5281/zenodo.1185084, 2018. a, bZargar, A., Sadiq, R., Naser, B., and Khan, F. I.: A review of drought indices, Environ. Rev., 19, 333–349, 2011. 

    Assessing water reservoirs management and development in Northern Vietnam

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    Abstract. In many developing countries water is a key renewable resource to complement carbon-emitting energy production and support food security in the face of demand pressure from fast-growing industrial production and urbanization. To cope with undergoing changes, water resources development and management have to be reconsidered by enlarging their scope across sectors and adopting effective tools to analyze current and projected infrastructure potential and operation strategies. In this paper we use multi-objective deterministic and stochastic optimization to assess the current reservoir operation and planned capacity expansion in the Red River Basin (Northern Vietnam), and to evaluate the potential improvement by the adoption of a more sophisticated information system. To reach this goal we analyze the historical operation of the major controllable infrastructure in the basin, the HoaBinh reservoir on the Da River, explore re-operation options corresponding to different tradeoffs among the three main objectives (hydropower production, flood control and water supply), using multi-objective optimization techniques, namely Multi-Objective Genetic Algorithm. Finally, we assess the structural system potential and the need for capacity expansion by application of Deterministic Dynamic Programming. Results show that the current operation can only be relatively improved by advanced optimization techniques, while investment should be put into enlarging the system storage capacity and exploiting additional information to inform the operation

    Contrasting non-dynamic and dynamic models of the water-energy nexus in small, off-grid Mediterranean islands

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    Water and energy supply in small Mediterranean islands are strictly interrelated and face a large number of challenging issues, mainly caused by the distance from the mainland, the lack of accessible and safe potable water sources, and the high seasonal variability of the water and energy demands driven by touristic fluxes. The energy system generally relies on carbon intensive, expensive stand-alone diesel generators, while potable water supply is provided by tank vessels. Although this combination provides essential services for local communities, it is often economically and environmentally unsustainable due to high operational costs and greenhouse gas (GHG) emissions. A traditional approach to improve the sustainability and the efficiency of the water and energy systems is to couple renewable energy sources (RES) with water supply technologies (e.g., desalination), in order to obtain efficient planning solutions (i.e. RES capacity, desalination plant capacity) in a least-cost fashion. However, this approach is generally non-dynamic and optimizes the power allocation using fixed electricity loads as a surrogate of the actual water demand supplied by the desalination plant through the water distribution network. Although this load reflects the actual water demand on the long-term (i.e. monthly or annual time scale), it could strongly deviate from the real water demand if we consider shorter time scales (i.e. daily or hourly), over which the water distribution network is able to store and move water in space and time. In this work, we comparatively analyse this traditional non-dynamic model of the water-energy nexus with a novel dynamic modelling approach, where the operation of both the nexus components (i.e. power allocation and operations of the water distribution network) is conjunctively optimized with respect to multiple economic and sustainability indicators (e.g., net present costs, GHG emissions, water supply deficit, RES penetration). This comparative analysis is performed over the real case study of the Italian Ustica island in the Mediterranean Sea. Preliminary results show the effectiveness of the dynamic approach in improving the static solution with respect to almost all the system performance metrics considered

    Lack of coupling of D-2 receptors to adenylate cyclase in GH-3 cells exposed to epidermal growth factor. Possible role of a differential expression of Gi protein subtypes.

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    Exposure of GH-3 cells to epidermal growth factor for 4 consecutive days induced the expression of both D-2(415) and D-2(444) dopamine-receptor isoforms. Epidermal growth factor also promoted a remarkable increase in the content of Gi3 protein, which is responsible for receptor-induced activation of potassium channels in GH-3 cells. D-2 receptors in this model apparently activate a specific transducing pathway, leading to opening of potassium channels and inhibition of prolactin release by cAMP-independent mechanisms. This is shown by: 1) the selective D-2 agonist quinpirole, while inactive on vasoactive intestinal peptide-induced prolactin release, strongly inhibited the hormone secretion induced by neurotensin; 2) quinpirole, up to 100 microM, did not inhibit cAMP production evoked by vasoactive intestinal peptide both in intact cells and in broken cell membrane preparations; and 3) quinpirole and other D-2 agonists strongly potentiated Rb+ efflux when measured in a nominally calcium-free reaction solution containing 100 mM potassium (voltage-dependent component), but did not modify Rb+ efflux if measured in a reaction solution containing 1 mM calcium and 5 mM potassium (calcium-activated, cAMP-dependent component)

    Prevalence and Mean Intensity of Anisakidae Parasite in Seafood Caught in Mediterranean Sea Focusing on Fish Species at Risk of Being Raw-consumed. A Meta Analysis and Systematic Review

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    Objective: to assess the prevalence and the mean intensity of anisakids in seafood caught in Mediterranean sea, focusing on fish species at risk of being raw-consumed. Design: Systematic review and meta-analysis of studies published 1960-2012. Study selection: main criteria for inclusion of studies were: findings of anisakids larvae, both in muscles and viscera; fish species for human consumption, caught in Mediterranean Sea; prevalence and mean intensity data for each species; sample size equal to or more than 40 fishes. Results: twelve studies were identified. Among them four studies considered fish species which are often consumed raw or lightly preserved or not thoroughly cooked anchovy, pilchard and Atlantic mackerel. Data synthesis: all pooled analyses were based on random-effect model. Anisakids prevalence in fish muscle was 0.64% (P < 0.0001), in viscera was 1.34% (P < 0.0001); overall was 0.95% (P < 0.0001). Mean intensity in muscle was 2.31 (P = 0.0083), in viscera was 1.55 (P = 0.0174), overall was 1.81 (P < 0.0005). Heterogeneity indexes (I2) were significantly high with the exception of viscera mean intensity. Conclusions: anchovy, pilchard, Atlantic mackerel have a low prevalence and mean intensity of Anisakidae larvae both in viscera and in muscle. Mean Intensity is low as well

    Predicted gamma-ray image of SN 1006 due to inverse Compton emission

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    We propose a method to synthesize the inverse Compton (IC) Îł-ray image of a supernova remnant starting from the radio (or hard X-ray) map and using results of the spatially resolved X-ray spectral analysis. The method is successfully applied to SN 1006. We found that synthesized IC Îł-ray images of SN 1006 show morphology in nice agreement with that reported by the High Energy Stereoscopic System (HESS) collaboration. The good correlation found between the observed very high energy Îł-ray and X-ray/radio appearance can be considered as evidence of the fact that the Îł-ray emission of SN 1006 observed by HESS is leptonic in origin, although a hadronic origin may not be excluded.Fil: Petruk, O.. Institute for Applied Problems in Mechanics and Mathematics; UcraniaFil: Bocchino, F.. Istituto Nazionale Di AstrofĂ­sica. Osservatorio AstronĂłmico Di Palermo; ItaliaFil: Miceli, M.. Istituto Nazionale Di AstrofĂ­sica. Osservatorio AstronĂłmico Di Palermo; ItaliaFil: Dubner, Gloria Mabel. Consejo Nacional de InvestigaciĂłnes CientĂ­ficas y TĂ©cnicas. Oficina de CoordinaciĂłn Administrativa Ciudad Universitaria. Instituto de AstronomĂ­a y FĂ­sica del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de AstronomĂ­a y FĂ­sica del Espacio; ArgentinaFil: Castelletti, Gabriela Marta. Consejo Nacional de InvestigaciĂłnes CientĂ­ficas y TĂ©cnicas. Oficina de CoordinaciĂłn Administrativa Ciudad Universitaria. Instituto de AstronomĂ­a y FĂ­sica del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de AstronomĂ­a y FĂ­sica del Espacio; ArgentinaFil: Orlando, S.. Istituto Nazionale Di AstrofĂ­sica. Osservatorio AstronĂłmico Di Palermo; ItaliaFil: Iakubovskyi, D.. Bogolyubov Institute for Theoretical Physics; UcraniaFil: Telezhinsky, I.. Kiev National Taras Shevchenko University; Ucrani

    Effective Study: Development and Application of a Question-Driven, Time-Effective Cardiac Magnetic Resonance Scanning Protocol

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    BACKGROUND: Long scanning times impede cardiac magnetic resonance (CMR) clinical uptake. A “one‐size‐fits‐all” shortened, focused protocol (eg, only function and late‐gadolinium enhancement) reduces scanning time and costs, but provides less information. We developed 2 question‐driven CMR and stress‐CMR protocols, including tailored advanced tissue characterization, and tested their effectiveness in reducing scanning time while retaining the diagnostic performances of standard protocols. METHODS AND RESULTS: Eighty three consecutive patients with cardiomyopathy or ischemic heart disease underwent the tailored CMR. Each scan consisted of standard cines, late‐gadolinium enhancement imaging, native T1‐mapping, and extracellular volume. Fat/edema modules, right ventricle cine, and in‐line quantitative perfusion mapping were performed as clinically required. Workflow was optimized to avoid gaps. Time target was 30% (CMR: from 42±8 to 28±6 minutes; stress‐CMR: from 50±10 to 34±6 minutes, both P45% of cases. Quality grading was similar between the 2 protocols. Tailored protocols did not require additional staff. CONCLUSIONS: Tailored CMR and stress‐CMR protocols including advanced tissue characterization are accurate and time‐effective for cardiomyopathies and ischemic heart diseas
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