31 research outputs found

    A Postprocessing methodology for direct normal irradiance forecasting using cloud information and aerosol load forecasts

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    A method for direct normal irradiance (DNI) forecasting for specific sites is proposed. It is based on the combination of a numerical weather prediction (NWP) model, which provides cloud information, with radiative transfer simulations fed with external aerosol forecasts. The NWP model used is the ECMWF Integrated Forecast System, and the radiative transfer information has been obtained from the Library of Radiative Transfer (libRadtran). Two types of aerosol forecasts have been tested: the global Monitoring Atmospheric Composition and Climate (MACC) model, which predicts five major components of aerosols, and the Dust Regional Atmospheric Model (BSC-DREAM8b) added to a fixed background calculated as the 20th percentile of the monthly mean of AERONET 2.0 observations from a different year. The methodology employed is valid for all meteorological situations, providing a stable and continuous DNI curve. The performance of the combined method has been evaluated against DNI observations and compared with the pure ECMWF forecasts at eight locations in the southern half of mainland Spain and the Canary Islands, which received high loadings of African dust for 2013 and 2014. Results for 1-day forecasts are presented. Although clouds play a major role, aerosols have a significant effect, but at shorter time scales. The combination of ECMWF and MACC forecasts gives the best global results, improving the DNI forecasts in events with high aerosol content. The regional BSC-DREAM8b yields good results for some extremely high dust conditions, although more reliable predictions, valid for any aerosol conditions, are provided by the MACC model

    Guanine nucleotide binding to the Bateman domain mediates the allosteric inhibition of eukaryotic IMP dehydrogenases

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    Inosine-5'-monophosphate dehydrogenase (IMPDH) plays key roles in purine nucleotide metabolism and cell proliferation. Although IMPDH is a widely studied therapeutic target, there is limited information about its physiological regulation. Using Ashbya gossypii as a model, we describe the molecular mechanism and the structural basis for the allosteric regulation of IMPDH by guanine nucleotides. We report that GTP and GDP bind to the regulatory Bateman domain, inducing octamers with compromised catalytic activity. Our data suggest that eukaryotic and prokaryotic IMPDHs might have developed different regulatory mechanisms, with GTP/GDP inhibiting only eukaryotic IMPDHs. Interestingly, mutations associated with human retinopathies map into the guanine nucleotide-binding sites including a previously undescribed non-canonical site and disrupt allosteric inhibition. Together, our results shed light on the mechanisms of the allosteric regulation of enzymes mediated by Bateman domains and provide a molecular basis for certain retinopathies, opening the door to new therapeutic approaches

    Unprecedented pathway of reducing equivalents in a diflavin-linked disulfide oxidoreductase

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    Flavoproteins participate in a wide variety of physiologically relevant processes that typically involve redox reactions. Within this protein superfamily, there exists a group that is able to transfer reducing equivalents from FAD to a redox-active disulfide bridge, which further reduces disulfide bridges in target proteins to regulate their structure and function. We have identified a previously undescribed type of flavin enzyme that is exclusive to oxygenic photosynthetic prokaryotes and that is based on the primary sequence that had been assigned as an NADPH-dependent thioredoxin reductase (NTR). However, our experimental data show that the protein does not transfer reducing equivalents from flavins to disulfides as in NTRs but functions in the opposite direction. High-resolution structures of the protein from Gloeobacter violaceus and Synechocystis sp. PCC6803 obtained by X-ray crystallography showed two juxtaposed FAD molecules per monomer in redox communication with an active disulfide bridge in a variant of the fold adopted by NTRs. We have tentatively named the flavoprotein “DDOR” (diflavin-linked disulfide oxidoreductase) and propose that its activity is linked to a thiol-based transfer of reducing equivalents in bacterial membranes. These findings expand the structural and mechanistic repertoire of flavoenzymes with oxidoreductase activity and pave the way to explore new protein engineering approaches aimed at designing redox-active proteins for diverse biotechnological applications

    Neprilysin inhibition, endorphin dynamics, and early symptomatic improvement in heart failure : a pilot study

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    Altres ajuts: This work was supported in part by Fundació La Marató de TV3 (201516-10, 201502-30), Societat Catalana de Cardiologia, "la Caixa" Banking Foundation.Sacubitril/valsartan is a first-in-class angiotensin receptor-neprilysin inhibitor developed for the treatment of heart failure with reduced ejection fraction. Its benefits are achieved through the inhibition of neprilysin (NEP) and the specific blockade of the angiotensin receptor AT1. The many peptides metabolized by NEP suggest multifaceted potential consequences of its inhibition. We sought to evaluate the short-term changes in serum endorphin (EP) values and their relation with patients' physical functioning after initiation of sacubitril/valsartan treatment. A total of 105 patients with heart failure with reduced ejection fraction, who were candidates for sacubitril/valsartan treatment, were included in this prospective, observational, multicentre, and international study. In a first visit, and in agreement with current guidelines, treatment with angiotensin-converting enzyme inhibitors or angiotensin receptor blocker was replaced by sacubitril/valsartan because of clinical indication by the responsible physician. By protocol, patients were reevaluated at 30 days after the start of sacubitril/valsartan. Serum levels of α- (α-EP), γ-Endorphin (γ-EP), and soluble NEP (sNEP) were measured using enzyme-linked immunoassays. New York Heart Association (NYHA) functional class was used as an indicator of patient's functional status. Baseline median levels of circulating α-EP, γ-EP, and sNEP were 582 (160-772), 101 (37-287), and 222 pg/mL (124-820), respectively. There was not a significant increase in α-EP nor γ-EP serum values after sacubitril/valsartan treatment (P value = 0.194 and 0.102, respectively). There were no significant differences in sNEP values between 30 days and baseline (P value = 0.103). Medians (IQR) of Δα-EP, Δγ-EP, and ΔsNEP between 30 days and baseline were 9.3 (−34 − 44), −3.0 (−46.0 − 18.9), and 0 units (−16.4 − 157.0), respectively. In a pre-post sacubitril/valsartan treatment comparison, there was a significant improvement in NYHA class, with 36 (34.3%) patients experiencing improvement by at least one NYHA class category. Δα-EP and ΔsNEP showed to be significantly associated with NYHA class after 30 days of treatment (P = 0.014 and P < 0.001, respectively). Δα-EP was linear and significantly associated with NYHA class improvement after 30 days of sacubitril/valsartan treatment. These preliminary data suggest that beyond the haemodynamic benefits achieved with sacubitril/valsartan, the altered cleavage of endorphin peptides by NEP inhibition may participate in patients' symptoms improvement

    Survival and dispersal routes of head-started loggerhead sea turtle (Caretta caretta) post-hatchlings in the Mediterranean Sea

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    [EN] Several loggerhead sea turtle (Caretta caretta) nesting events have been recorded along Spain's Mediterranean coast, outside its known nesting range, in recent years. In view of the possible expansion of its nesting range and considering the conservation status of this species, management measures like nest protection and head-start programs have been implemented. To study the dispersal behavior and survival of head-started loggerheads, 19 post-hatchlings from three nesting events were satellite tracked after their release in three consecutive years (2015-2017). This paper presents the first study of survival probabilities and dispersal movements of loggerhead post-hatchlings in the Mediterranean basin. Monitored post-hatchlings dispersed over large areas using variable routes, mainly off the continental shelf. Nonetheless, post-hatchlings dispersed to high-productivity warmer areas during the coldest months of monitoring. These areas might be optimum for their survival and development. We observed differences regarding dispersal orientation and routes among individuals, even from the same nest, release date, and location. Our survival models contributed to improving current survival estimates for sea turtle post-hatchlings. We observed a high probability of survival in head-started individuals during the first months after release, usually the most critical period after reintroduction. The data did not support an effect of habitat (neritic or oceanic) in survival, or an effect of the region (Balearic sea or Alboran sea) in survival probability. Differences in survival between nests were observed. These differences might be related to parasitic infections suffered during the head-starting period. This study shows that nest management measures may contribute to the conservation and range expansion of the loggerhead turtle population in the western Mediterranean.This satellite study was funded by Universitat Politecnica de Valencia, Ministerio de Agricultura y Medio Ambiente (ref: 16MNSV006), Ministerio de Economia, Industria y Competitividad (ref: CGL2011-30413), Fundacion CRAM, Fundacion Hombre y Territorio and Eduardo J. Belda. Corresponding author, S. Abalo, was supported by a Ph.D. grant (FPU) from Ministerio de Educacion, Cultura y Deporte (Spain). J. Tomas is also supported by project Prometeo II (2015) of Generalitat Valenciana and project INDICIT of the European Commission, Environment Directorate-General. We are extremely thankful to the entities that have collaborated: we thank all professionals at the Oceanografic, especially at the ARCA Rehabilitation Center, for their many efforts and whole-hearted dedication to the best animal care. In particular, we are grateful to the Conselleria d'Agricultura, Medi Ambient, Canvi Climatic i Desenvolupament Rural of the Valencia Community Regional Government. We also thank the professionals at Centro de Recuperacion de Animales Marinos (CRAM) for their dedication and animal care. We are thankful to the Marine Zoology Unit of the University of Valencia, NGO Xaloc, EQUINAC, Aquarium of Sevilla, Donana Biological Station (EBD-CSIC) and to involved professionals at Consejeria de Medio Ambiente y Ordenacion del Territorio (CMAOT) of Junta de Andalucia, especially at the Andalusian Marine Environment Management Center (CEGMA) for their efforts with animal care, logistics for release events and necropsy of "Rabiosa". We are particularly grateful to the people who called 112 to report a nesting event and to the nest custody volunteers. Thanks are due to the staff of Parador de El Saler for volunteering logistical support. The authors wish to acknowledge the use of the Maptool program for analysis and graphics in this paper. Maptool is a product of SEATURTLE.ORG (Information is available at www.seaturtle.org). Also, we acknowledge the use of the Douglas Argos Filter (DAF) utility in Movebank (www.movebank.org) and especially David Douglas for his help and recommendations. Finally, we thank the reviewers for their reviewing efforts.Abalo-Morla, S.; Marco, A.; Tomás, J.; Revuelta, O.; Abella, E.; Marco, V.; Crespo-Picazo, J.... (2018). Survival and dispersal routes of head-started loggerhead sea turtle (Caretta caretta) post-hatchlings in the Mediterranean Sea. Marine Biology. 165(3). https://doi.org/10.1007/s00227-018-3306-2S1653Abella P, Marco A, Martins S, Hawkes LA (2016) Is this what a climate change-resilient population of marine turtles looks like? Biol Conserv 193:124–132. https://doi.org/10.1016/j.biocon.2015.11.023Addison DS, Nelson KA (2000) Recapture of a tagged, captive reared juvenile loggerhead turtle—an example of habituation? Mar Turt Newsl 89:15–16Agostellini C, Lund U (2017) R package ‘circular’: Circular Statistics (version 0.4-93). https://r-forge.r-project.org/projects/circular/ . Accessed 05 July 2017Arendt MD, Schwenter JA, Boynton J, Segars AL, Byrd JI, David W, Parker L (2012) Temporal trends (2000–2011) and influences on fishery-independent catch rates for loggerhead sea turtles (Caretta caretta) at an important coastal foraging region in the southeastern United States. Fish Bull 110:470–483Armstrong DP, Seddon PJ (2008) Directions in reintroduction biology. Trends Ecol Evol 23:20–25. https://doi.org/10.1016/j.tree.2007.10.003Baez J, Macias D, Antonio Caminas J, Ortiz de Urbina JM, Garcia-Barcelona S, Jesus Bellido J, Real R (2013) By-catch frequency and size differentiation in loggerhead turtles as a function of surface longline gear type in the western Mediterranean Sea. J Mar Biol Assoc UK 93:1423–1427. https://doi.org/10.1017/S0025315412001841Balbín R, Flexas MM, López-Jurado JL, Peña M, Amores A, Alemany F (2012) Vertical velocities and biological consequences at a front detected at the balearic sea. Cont Shelf Res 47:28–41. https://doi.org/10.1016/j.csr.2012.06.008Balbín R, López-Jurado JL, Flexas MM, Reglero P, Vélez-Velchí P, González-Pola C, Rodríguez JM, García A, Alemany F (2014) Interannual variability of the early summer circulation around the Balearic Islands: driving factors and potential effects on the marine ecosystem. J Mar Syst 138:70–81. https://doi.org/10.1016/j.jmarsys.2013.07.004Batschelet E (1981) Circular statistics in biology. Academic Press, LondonBell C, Parsons J (2002) Cayman turtle farm head-starting project yields tangible success. Mar Turt Newsl 98:5–6Bjorndal K, Bolten A, Martins H (2000) Somatic growth model of juvenile loggerhead sea turtles Caretta caretta: duration of pelagic stage. Mar Ecol Prog Ser 202:265–272. https://doi.org/10.3354/meps202265Bolten B (2003) Variation in sea turtle life history patterns: neritic vs. oceanic developmental stages. In: Lutz PL, Musick J, Wyneken J (eds) The biology of sea turtles. CRC Press, Boca Ratón, pp 243–257Bowen BW, Karl SA (2007) Population genetics and phylogeography of sea turtles. Mol Ecol 16:4886–4907. https://doi.org/10.1111/j.1365-294X.2007.03542.xBowen B, Avise JC, Richardson JI, Meylan AB, Margaritoulis D, Hopkins-Murphy SR (1993) Population Structure of loggerhead turtles (Caretta caretta) in the Northwestern Atlantic Ocean and Mediterranean Sea. Conserv Biol 7:834–844. https://doi.org/10.1046/j.1523-1739.1993.740834.xBriscoe D, Parker D, Balazs GH, Kurita M, Saito T, Okamoto H, Rice M, Polovina JJ, Crowder LB (2016) Active dispersal in loggerhead sea turtles (Caretta caretta) during the ‘lost years’. Proc R Soc B Biol Sci 283:1832. https://doi.org/10.1098/rspb.2016.0690Burke R (2015) Head-starting turtles: learning from experience. ‎Herpetol Conserv Biol 10(1):299–308Burnham KP, Anderson DR (1998) Model selection and inference: a practical information-theoretic approach. Springer, New YorkCalenge C (2006) The package ‘adehabitat’ for the R software: a tool for the analysis of space and habitat use by animals. Ecol Model 197:516–519. https://doi.org/10.1016/j.ecolmodel.2006.03.017Cardona L, Hays GC (2018) Ocean currents, individual movements and genetic structuring of populations. Mar Biol 165:10. https://doi.org/10.1007/s00227-017-3262-2Cardona L, Revelles M, Carreras C, San Félix M, Gazo M, Aguilar A (2005) Western Mediterranean immature loggerhead turtles: habitat use in spring and summer assessed through satellite tracking and aerial surveys. Mar Biol 147:583–591. https://doi.org/10.1007/s00227-005-1578-9Cardona L, Revelles M, Parga ML, Tomás J, Aguilar A, Alegre F, Raga A, Ferrer X (2009) Habitat use by loggerhead sea turtles Caretta caretta off the coast of eastern Spain results in a high vulnerability to neritic fishing gear. Mar Biol 156:2621–2630. https://doi.org/10.1007/s00227-009-1288-9Cardona L, Fernández G, Revelles M, Aguilar A (2012) Readaptation to the wild of rehabilitated loggerhead sea turtles (Caretta caretta) assessed by satellite telemetry. Aquatic Conserv Mar Freshw Ecosyst 22:104–112. https://doi.org/10.1002/aqc.1242Carr A (1987) New perspectives on the pelagic stage of sea turtle development. Conserv Biol 1:103–121. https://doi.org/10.1111/j.1523-1739.1987.tb00020.xCarreras C, Cardona L, Aguilar A (2004) Incidental catch of the loggerhead turtle Caretta caretta off the Balearic Islands (western Mediterranean). Biol Conserv 117:321–329. https://doi.org/10.1016/j.biocon.2003.12.010Carreras C, Pascual M, Tomás J, Marco A, Hochscheid S, Bellido J, Gozalbes P, Parga M, Piovano S, Cardona L (2015) From accidental nesters to potential colonisers, the sequencial colonisation of the mediterranean by the loggerhead sea turtle (Caretta caretta). In: Kaska Y, Sonmez B, Turkecan O, Sezgin C. Book of abstracts of 35th Annual Symposium on Sea Turtle Biology and Conservation. MACART press, TurkeyCasale P (2011) Sea turtle by-catch in the Mediterranean. Fish Fish 12:299–316. https://doi.org/10.1111/j.1467-2979.2010.00394.xCasale P, Heppell S (2016) How much sea turtle bycatch is too much? A stationary age distribution model for simulating population abundance and potential biological removal in the Mediterranean. Endanger Species Res 29:239–254. https://doi.org/10.3354/esr00714Casale P, Margaritoulis D (2010) Sea turtles in the Mediterranean: distribution, threats and conservation priorities. IUCN, GlandCasale P, Mariani P (2014) The first ‘lost year’ of Mediterranean Sea turtles: dispersal patterns indicate subregional management units for conservation. Mar Ecol Prog Ser 498:263–274. https://doi.org/10.3354/meps10640Casale P, Tucker AD (2015) Caretta caretta. The IUCN Red List of Threatened Species 2015: e.T3897A83157651. http://dx.doi.org/10.2305/IUCN.UK.2015-4.RLTS.T3897A83157651.en . Accessed 29 March 2017Casale P, Mazaris AD, Freggi D, Basso R, Argano R (2007) Survival probabilities of loggerhead sea turtles (Caretta caretta) estimated from capture-mark-recapture data in the Mediterranean Sea. Sci Mar 71:365–372Casale P, Mazaris AD, Freggi D, Vallini C, Argano R (2009) Growth rates and age at adult size of loggerhead sea turtles (Caretta caretta) in the Mediterranean Sea, estimated through capture-mark-recapture records. Sci Mar 73:589–595. https://doi.org/10.3989/scimar.2009.73n3589Casale P, Mazaris A, Freggi D (2011) Estimation of age at maturity of loggerhead sea turtles Caretta caretta in the Mediterranean using length-frequency data. Endanger Species Res 13:123–129. https://doi.org/10.3354/esr00319Casale P, Freggi D, Furii G, Vallini C, Salvemini P, Deflorio M, Totaro G, Raimondi S, Fortuna C, Godley BJ (2015) Annual survival probabilities of juvenile loggerhead sea turtles indicate high anthropogenic impact on Mediterranean populations. Aquatic Conserv Mar Freshw Ecosyst 25:551–561. https://doi.org/10.1002/aqc.2467Choquet R, Lebreton JD, Gimenez O, Reboulet AM, Pradel R (2009) U-CARE: Utilities for performing goodness of fit tests and manipulating CApture–REcapture data. Ecography 32:1071–1074. https://doi.org/10.1111/j.1600-0587.2009.05968.xChristiansen F, Putman NF, Farman R, Parker DM, Rice MR, Polovina JJ, Balazs GH, Hays GC (2016) Spatial variation in directional swimming enables juvenile sea turtles to reach and remain in productive waters. Mar Ecol Prog Ser 557:247–259. https://doi.org/10.3354/meps11874CLS (2016) Argos User’s Manual. http://www.argos-system.org/manual/3-location/34_location_classes.htm . Accessed 8 Sep 2016Clusa M, Carreras C, Pascual M, Demetropoulos A, Margaritoulis D, Rees AF, Hamza AA, Khalil M, Aureggi M, Levy Y, Türkozan O, Marco A, Aguilar A, Cardona L (2013) Mitochondrial DNA reveals Pleistocenic colonisation of the Mediterranean by loggerhead turtles (Caretta caretta). J Exp Mar Biol Ecol 439:15–24. https://doi.org/10.1016/j.jembe.2012.10.011Clusa M, Carreras C, Pascual M, Gaughran SJ, Piovano S, Giacoma C, Fernández G, Levy Y, Tomás J, Raga JA, Maffucci F, Hochscheid S, Aguilar A, Cardona L (2014) Fine-scale distribution of juvenile Atlantic and Mediterranean loggerhead turtles (Caretta caretta) in the Mediterranean Sea. Mar Biol 161:509–519. https://doi.org/10.1007/s00227-013-2353-yColes W, Musick JA (2000) Satellite sea surface temperature analysis and correlation with sea turtle distribution off North Carolina. Copeia 2000:551–554. https://doi.org/10.1643/0045-8511(2000)000[0551:SSSTAA]2.0.CO;2Conant TA, Dutton PH, Eguchi T Epperly SP, Fahy CC, Godfrey MH, MacPherson SL, Possardt EE, Schroeder BA, Seminoff JA, Snover ML, Upite CM, Witherington BE (2009) Loggerhead sea turtle (Caretta caretta) 2009 status review under the US Endangered Species Act. Report of the Loggerhead Biological Review Team to the National Marine Fisheries Service, August 2009. NOAA Institutional Repository. https://repository.library.noaa.gov/view/noaa/16204 . Accessed 1 January 2018Coyne M, Godley B (2005) Satellite tracking and analysis tool (STAT): an integrated system for archiving, analyzing and mapping animal tracking data. Mar Ecol Prog Ser 301:1–7Crespo-Picazo JL, García-Párraga D, Domènech F, Tomás J, Aznar FJ, Ortega J, Corpa JM (2017) Parasitic outbreak of the copepod Balaenophilus manatorum in neonate loggerhead sea turtles (Caretta caretta) from a head-starting program. BMC Vet Res 13:154. https://doi.org/10.1186/s12917-017-1074-8Cribb TH, Crespo-Picazo JL, Cutmore SC, Stacy BA, Chapman PA, García-Párraga D (2017) Elucidation of the first definitively identified life cycle for a marine turtle blood fluke (Trematoda: Spirorchiidae) enables informed control. Int J Parasitol 47:61–67. https://doi.org/10.1016/j.ijpara.2016.11.002Delaugerre M, Cesarini C (2004) Confirmed nesting of the loggerhead turtle in Corsica. Mar Turt Newsl 104:12Demetropoulos A (2003) Impact of tourism development on marine turtle nesting: strategies and actions to minimise impact. In: Margaritoulis D, Demetropoulos A (eds) Proceedings of the First Mediterranean Conference on Marine Turtles. Barcelona Convention—Bern Convention—Bonn Convention (CMS). Nicosia, p 27–36Domènech F, Badillo FJ, Tomás J, Raga JA, Aznar FJ (2015) Epibiont communities of loggerhead marine turtles (Caretta caretta) in the western Mediterranean: influence of geographic and ecological factors. J Mar Biol Assoc UK 95:851–861. https://doi.org/10.1017/S0025315414001520Domènech F, Tomás J, Crespo-Picazo JL, García-Párraga D, Raga JA, Aznar FJ (2017) To swim or not to swim: potential transmission of Balaenophilus manatorum (Copepoda: Harpacticoida) in marine turtles. PLoS One 12:e0170789. https://doi.org/10.1371/journal.pone.0170789Douglas DC, Weinzierl R, Davidson CS, Kays R, Wikelski M, Bohrer G (2012) Moderating Argos location errors in animal tracking data. Methods Ecol Evol 3:999–1007. https://doi.org/10.1111/j.2041-210X.2012.00245.xEchwikhi K, Jribi I, Bradai MN, Bouain A (2012) Overview of loggerhead turtles coastal nets interactions in the Mediterranean Sea. Aquatic Conserv Mar Freshw Ecosyst 22:827–835. https://doi.org/10.1002/aqc.2270Gaube P, Barceló C, McGillicuddy DJ, Domingo A, Miller P, Giffoni B, Marcovaldi N, Swimmer Y (2017) The use of mesoscale eddies by juvenile loggerhead sea turtles (Caretta caretta) in the southwestern Atlantic. PLoS One 12:e0172839. https://doi.org/10.1371/journal.pone.0172839Godley BJ, Broderick AC, Glen F, Hays GC (2003) Post-nesting movements and submergence patterns of loggerhead marine turtles in the Mediterranean assessed by satellite tracking. J Exp Mar Biol Ecol 287:119–134. https://doi.org/10.1016/S0022-0981(02)00547-6González C, Bruno I, Maxwell S, Álvarez K, Albareda D, Acha EM, Campagna C (2016) Habitat use, site fidelity and conservation opportunities for juvenile loggerhead sea turtles in the Río de la Plata, Argentina. Mar Biol 163:1–13. https://doi.org/10.1007/s00227-015-2795-5Gueguen L (2000) Segmentation by maximal predictive partitioning according to composition biases. In: Gascuel O, Sagot MF (eds) Computational biology. lecture notes in computer science, 2066th edn. Springer, Berlin, pp 32–44Hays GC (2000) The implications of variable remigration intervals for the assessment of population size in marine turtles. J Therm Biol 206:221–227. https://doi.org/10.1006/jtbi.2000.2116Hays GC, Marsh R (1997) Estimating the age of juvenile loggerhead sea turtles in the North Atlantic. Can J Zool 75:40–46. https://doi.org/10.1139/z97-005Hays GC, Akesson S, Godley BJ, Luschi P, Santidrian P (2001) The implications of location accuracy for the interpretation of satellite-tracking data. Anim Behav 61:1035–1040. https://doi.org/10.1006/anbe.2001.1685Hays GC, Fossette S, Katselidis KA, Mariani P, Schofield G (2010) Ontogenetic development of migration: lagrangian drift trajectories suggest a new paradigm for sea turtles. J R Soc Interface 7:1319–1327. https://doi.org/10.1098/rsif.2010.0009Hays GC, Ferreira LC, Sequeira AMM, Meekan MG, Duarte CM, Bailey H, Bailleul F, Bowen WD, Caley MJ, Costa DP, Eguíluz VM, Fossette S, Friedlaender AS, Gales N, Gleiss AC, Gunn J, Harcourt R, Hazen EL, Heithaus MR, Heupel M, Holland K, Horning M, Jonsen I, Kooyman GL, Lowe CG, Madsen PT, Marsh H, Phillips RA, Righton D, Ropert-Coudert Y, Sato K, Shaffer SA, Simpfendorfer CA, Sims DW, Skomal G, Takahashi A, Trathan PN, Wikelski M, Womble JN, Thums M (2016) Key questions in marine megafauna movement ecology. Trends Ecol Evol 31:463–475. https://doi.org/10.1016/j.tree.2016.02.015Hazen EL, Maxwell SM, Bailey H, Bograd SJ, Hamann M, Gaspar P, Godley BJ, Shillinger GL (2012) Ontogeny in marine tagging and tracking science: technologies and data gaps. Mar Ecol Prog Ser 457:221–240. https://doi.org/10.3354/meps09857Heppell SS (1998) Application of life-history theory and population model analysis to turtle conservation. Copeia 1998:367–375. https://doi.org/10.2307/1447430Heppell SS, Crowder LB, Crouse DT (1996) Models to evaluate headstarting as a management tool for long-lived turtles. Ecol Appl 6:556–565. https://doi.org/10.2307/2269391Hines JE, Sauer JR (1989) Program CONTRAST–A general program for the analysis of several survival or recovery rate estimates. Fish and Wildlife Technical Report, 24Kobayashi DR, Farman R, Polovina JJ, Parker DM, Rice M, Balazs GH (2014) “Going with the Flow” or not: evidence of positive rheotaxis in oceanic juvenile loggerhead turtles (Caretta caretta) in the South Pacific Ocean using satellite tags and ocean circulation data. PLoS One 9:e103701. https://doi.org/10.1371/journal.pone.0103701Kornaraki E, Matossian DA, Mazaris AD, Matsinos YG, Margaritoulis D (2006) Effectiveness of different conservation measures for loggerhead sea turtle (Caretta caretta) nests at Zakynthos Island, Greece. Biol Conserv 130:324–330. https://doi.org/10.1016/j.biocon.2005.12.027Lamont MM, Putman NF, Fujisaki I, Hart KM (2015) Spatial requirements of different life-stages of the loggerhead turtle (Caretta caretta) from a distinct population segment in the northern Gulf of Mexico. Herpetol Conserv Biol 10:2643Lebreton J-D, Burnham KP, Clobert J, Anderson DR (1992) Modelling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecol Monogr 62:67–118. https://doi.org/10.2307/2937171Lohmann KJ, Putman NF, Lohmann CM (2012) The magnetic map of hatchling loggerhead sea turtles. Curr Opin Neurobiol 22:336–342. https://doi.org/10.1016/j.conb.2011.11.005Luschi P, Casale P (2014) Movement patterns of marine turtles in the Mediterranean Sea: a review. Ital J Zool 81:478–495. https://doi.org/10.1080/11250003.2014.963714Maffucci F, Corrado R, Palatella L, Borra M, Marullo S, Hochscheid S, Lacorata G, Iudicone D (2016) Seasonal heterogeneity of ocean warming: a mortality sink for ectotherm colonizers. Sci Rep 6:23983. https://doi.org/10.1038/srep23983MAGRAMA (2012) Estrategia Marina. Demarcación Marina Levantino-Balear, Parte I: Marco general, Evaluación inicial y buen estado ambiental. Ministerio de Agricultura, Alimentación y Medio Ambiente. http://www.mapama.gob.es/es/costas/temas/proteccion-medio-marino/I_Marco_General_Levantino-Balear_tcm7-204338.pdf . Accessed 29 March 2017Mansfield KL, Wyneken J, Rittschof D, Walsh M, Lim CW, Richards PM et al (2012) Satellite tag attachment methods for tracking neonate sea turtles. Mar Ecol Prog Ser 457:181–192. https://doi.org/10.3354/meps09485Mansfield KL, Wyneken J, Porter WP, Luo J (2014) First satellite tracks of neonate sea turtles redefine the ‘lost years’ oceanic niche. Proc R Soc B Biol Sci. https://doi.org/10.1098/rspb.2013.3039Mansfield KL, Mendilaharsu ML, Putman NF, dei Marcovaldi MAG, Sacco AE, Lopez G, Pires T, Swimmer Y (2017) First satellite tracks of South Atlantic sea turtle ‘lost years’: seasonal variation in trans-equatorial movement. Proc R Soc B 284:20171730. https://doi.org/10.1098/rspb.2017.1730Margaritoulis D, Argano R, Baran I, Bentivegna F, Bradai MN, Camiñas JA, Casale P (2003) Loggerhead turtles in the Mediterranean Sea: present knowledge and conservation perspectives. In: Bolten AB (ed) Loggerhead Sea Turtle, B.E. Witherington. Smithsonian Institution

    Effectiveness of an mHealth intervention combining a smartphone app and smart band on body composition in an overweight and obese population: Randomized controlled trial (EVIDENT 3 study)

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    Background: Mobile health (mHealth) is currently among the supporting elements that may contribute to an improvement in health markers by helping people adopt healthier lifestyles. mHealth interventions have been widely reported to achieve greater weight loss than other approaches, but their effect on body composition remains unclear. Objective: This study aimed to assess the short-term (3 months) effectiveness of a mobile app and a smart band for losing weight and changing body composition in sedentary Spanish adults who are overweight or obese. Methods: A randomized controlled, multicenter clinical trial was conducted involving the participation of 440 subjects from primary care centers, with 231 subjects in the intervention group (IG; counselling with smartphone app and smart band) and 209 in the control group (CG; counselling only). Both groups were counselled about healthy diet and physical activity. For the 3-month intervention period, the IG was trained to use a smartphone app that involved self-monitoring and tailored feedback, as well as a smart band that recorded daily physical activity (Mi Band 2, Xiaomi). Body composition was measured using the InBody 230 bioimpedance device (InBody Co., Ltd), and physical activity was measured using the International Physical Activity Questionnaire. Results: The mHealth intervention produced a greater loss of body weight (–1.97 kg, 95% CI –2.39 to –1.54) relative to standard counselling at 3 months (–1.13 kg, 95% CI –1.56 to –0.69). Comparing groups, the IG achieved a weight loss of 0.84 kg more than the CG at 3 months. The IG showed a decrease in body fat mass (BFM; –1.84 kg, 95% CI –2.48 to –1.20), percentage of body fat (PBF; –1.22%, 95% CI –1.82% to 0.62%), and BMI (–0.77 kg/m2, 95% CI –0.96 to 0.57). No significant changes were observed in any of these parameters in men; among women, there was a significant decrease in BMI in the IG compared with the CG. When subjects were grouped according to baseline BMI, the overweight group experienced a change in BFM of –1.18 kg (95% CI –2.30 to –0.06) and BMI of –0.47 kg/m2 (95% CI –0.80 to –0.13), whereas the obese group only experienced a change in BMI of –0.53 kg/m2 (95% CI –0.86 to –0.19). When the data were analyzed according to physical activity, the moderate-vigorous physical activity group showed significant changes in BFM of –1.03 kg (95% CI –1.74 to –0.33), PBF of –0.76% (95% CI –1.32% to –0.20%), and BMI of –0.5 kg/m2 (95% CI –0.83 to –0.19). Conclusions: The results from this multicenter, randomized controlled clinical trial study show that compared with standard counselling alone, adding a self-reported app and a smart band obtained beneficial results in terms of weight loss and a reduction in BFM and PBF in female subjects with a BMI less than 30 kg/m2 and a moderate-vigorous physical activity level. Nevertheless, further studies are needed to ensure that this profile benefits more than others from this intervention and to investigate modifications of this intervention to achieve a global effect

    Seguimiento de las guías españolas para el manejo del asma por el médico de atención primaria: un estudio observacional ambispectivo

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    Objetivo Evaluar el grado de seguimiento de las recomendaciones de las versiones de la Guía española para el manejo del asma (GEMA 2009 y 2015) y su repercusión en el control de la enfermedad. Material y métodos Estudio observacional y ambispectivo realizado entre septiembre del 2015 y abril del 2016, en el que participaron 314 médicos de atención primaria y 2.864 pacientes. Resultados Utilizando datos retrospectivos, 81 de los 314 médicos (25, 8% [IC del 95%, 21, 3 a 30, 9]) comunicaron seguir las recomendaciones de la GEMA 2009. Al inicio del estudio, 88 de los 314 médicos (28, 0% [IC del 95%, 23, 4 a 33, 2]) seguían las recomendaciones de la GEMA 2015. El tener un asma mal controlada (OR 0, 19, IC del 95%, 0, 13 a 0, 28) y presentar un asma persistente grave al inicio del estudio (OR 0, 20, IC del 95%, 0, 12 a 0, 34) se asociaron negativamente con tener un asma bien controlada al final del seguimiento. Por el contrario, el seguimiento de las recomendaciones de la GEMA 2015 se asoció de manera positiva con una mayor posibilidad de que el paciente tuviera un asma bien controlada al final del periodo de seguimiento (OR 1, 70, IC del 95%, 1, 40 a 2, 06). Conclusiones El escaso seguimiento de las guías clínicas para el manejo del asma constituye un problema común entre los médicos de atención primaria. Un seguimiento de estas guías se asocia con un control mejor del asma. Existe la necesidad de actuaciones que puedan mejorar el seguimiento por parte de los médicos de atención primaria de las guías para el manejo del asma. Objective: To assess the degree of compliance with the recommendations of the 2009 and 2015 versions of the Spanish guidelines for managing asthma (Guía Española para el Manejo del Asma [GEMA]) and the effect of this compliance on controlling the disease. Material and methods: We conducted an observational ambispective study between September 2015 and April 2016 in which 314 primary care physicians and 2864 patients participated. Results: Using retrospective data, we found that 81 of the 314 physicians (25.8%; 95% CI 21.3–30.9) stated that they complied with the GEMA2009 recommendations. At the start of the study, 88 of the 314 physicians (28.0%; 95% CI 23.4–33.2) complied with the GEMA2015 recommendations. Poorly controlled asthma (OR, 0.19; 95% CI 0.13–0.28) and persistent severe asthma at the start of the study (OR, 0.20; 95% CI 0.12–0.34) were negatively associated with having well-controlled asthma by the end of the follow-up. In contrast, compliance with the GEMA2015 recommendations was positively associated with a greater likelihood that the patient would have well-controlled asthma by the end of the follow-up (OR, 1.70; 95% CI 1.40–2.06). Conclusions: Low compliance with the clinical guidelines for managing asthma is a common problem among primary care physicians. Compliance with these guidelines is associated with better asthma control. Actions need to be taken to improve primary care physician compliance with the asthma management guidelines

    Enterococcus faecium small colony variant endocarditis in an immunocompetent patient

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    Small colony variants (SCV) are slow-growing subpopulations of bacteria usually associated with auxotrophism, causing persistent or recurrent infections. Enterococcus faecalis SCV have been seldom described, and only one case of Enterococcus faecium SCV has been reported, associated with sepsis in a leukaemia patient. Here we report the first case described of bacteraemia and endocarditis by SCV E. faecium in an immunocompetent patient
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