Morphosedimentary and phytogeography reconstruction of the middle section of the river Jarama (Madrid, Spain) during the second half of the Holocene.

[Abstract] Two sites located on the alluvial plain of the Jarama River, near Madrid, Spain, have been studied using geological, palynological and xylological techniques. Uniquely for this region, numerous wood subfossils of Alnus and Ulmus have been found together with an strobile of Pinus halepensis. This has allowed the stablishment of a coherent radiocarbon chronology, which demonstrates that these sedimentary environments began to develop during the mid Holocene. The dated sediments, which also contains appreciable amounts of pollen, have been deposited upon older palaeosols which has in turn developed directly on the geological substrate. Palynological analyses of these levels have provided valuable insights into the floristic composition of the communities associated with the different biotopes present in the area. As a result of these multiproxy analyses an interpretation of Holocene landscape history and vegetation dynamics is presente

Computer-aided diagnosis of multiple sclerosis using a support vector machine and optical coherence tomography features

The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of optic neuritis and forty-eight healthy control subjects were selected. Swept-source optical coherence tomography (SS-OCT) was performed using a DRI (deep-range imaging) Triton OCT device (Topcon Corp., Tokyo, Japan). Mean values (right and left eye) for macular thickness (retinal and choroidal layers) and peripapillary area (retinal nerve fibre layer, retinal, ganglion cell layer—GCL, and choroidal layers) were compared between both groups. Based on the analysis of the area under the receiver operator characteristic curve (AUC), the 3 variables with the greatest discriminant capacity were selected to form the feature vector. A SVM was used as an automatic classifier, obtaining the confusion matrix using leave-one-out cross-validation. Classification performance was assessed with Matthew’s correlation coefficient (MCC) and the AUCCLASSIFIER. The most discriminant variables were found to be the total GCL++ thickness (between inner limiting membrane to inner nuclear layer boundaries), evaluated in the peripapillary area and macular retina thickness in the nasal quadrant of the outer and inner rings. Using the SVM classifier, we obtained the following values: MCC = 0.81, sensitivity = 0.89, specificity = 0.92, accuracy = 0.91, and AUCCLASSIFIER = 0.97. Our findings suggest that it is possible to classify control subjects and MS patients without previous optic neuritis by applying machine-learning techniques to study the structural neurodegeneration in the retina

Dye-Loaded Quatsomes Exhibiting FRET as Nanoprobes for Bioimaging

Fluorescent organic nanoparticles (FONs) are emerging as an attractive alternative to the well-established fluorescent inorganic nanoparticles or small organic dyes. Their proper design allows one to obtain biocompatible probes with superior brightness and high photostability, although usually affected by low colloidal stability. Herein, we present a type of FONs with outstanding photophysical and physicochemical properties in-line with the stringent requirements for biomedical applications. These FONs are based on quatsome (QS) nanovesicles containing a pair of fluorescent carbocyanine molecules that give rise to Förster resonance energy transfer (FRET). Structural homogeneity, high brightness, photostability, and high FRET efficiency make these FONs a promising class of optical bioprobes. Loaded QSs have been used for in vitro bioimaging, demonstrating the nanovesicle membrane integrity after cell internalization, and the possibility to monitor the intracellular vesicle fate. Taken together, the proposed QSs loaded with a FRET pair constitute a promising platform for bioimaging and theranostics

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

[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

Continuous‑wavelet‑transform analysis of the multifocal ERG waveform in glaucoma diagnosis

The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals’ amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.Ministerio de Ciencia e Innovació

Improved measurement of intersession latency in mfVEPs

Purpose: The purpose of the study is to present a method (Selfcorr) by which to measure intersession latency differences between multifocal VEP (mfVEP) signals. Methods: The authors compared the intersession latency difference obtained using a correlation method (Selfcorr) against that obtained using a Template method. While the Template method cross-correlates the subject’s signals with a reference database, the Selfcorr method cross-correlates traces across subsequent recordings taken from the same subject. Results: The variation in latency between intersession signals was 0.8 ± 13.6 and 0.5 ± 5.0 ms for the Template and Selfcorr methods, respectively, with a coefficient of variability C V_TEMPLATE = 15.83 and C V_SELFCORR = 5.68 (n = 18, p = 0.0002, Wilcoxon). The number of analyzable sectors with the Template and Selfcorr methods was 36.7 ± 8.5 and 45.3 ± 8.7, respectively (p = 0.0001, paired t test, two tailed). Conclusions: The Selfcorr method produces smaller intersession mfVEP delays and variability over time than the Template method.Ministerio de Ciencia e Innovació

Investigaciones paleobotánicas en la cuenca central del Duero

El objetivo del trabajo es dar a conocer el estado actual de conocimientos científicos sobre el pasado del paisaje vegetal (Cuaternario final) en los territorios interiores no montanos de la depresión del Duero. Se recogen todos los yacimientos cuyo estudio ya ha concluido así como los que se encuentran en fase de investigación o prospección. Se precisa el tipo de informador en cada caso (polen, carbones, maderas, otros macrorrestos), el rango cronológico conocido hasta el momento así como el grado o proporción de trabajo realizado en cada yacimiento en relación con las previsiones efectuadas. Se aporta una síntesis-resumen de los principales resultados obtenidos hasta el momento y de los aspectos más concluyentes de los mismos en relación con la elaboración de modelos de evolución del paisaje vegetal posteriores al último máximo glacial en la Meseta norte. A nuestro juicio debe destacarse, como uno de los resultados más relevantes, el conocimiento ya afianzado de que los pinares de meseta han sido el elemento más significativo en amplios sectores del sur y este de la cuenca a lo largo de todo o gran parte del Holoceno, circunstancia que contrasta con todas las propuestas de paisaje pretérito (preantrópico) existentes antes de la realización de las prospecciones paleobotánicas

Nuevos datos de carbones y maderas fósiles de Pinus pinaster Aiton en el Holoceno de la Península Ibérica

The study of ligneous fossil remains (charcoal and wood) corresponding to three sites located in the interior of the Iberian Peninsula is presented. The chronologies established by means of radiocarbon or relative dating (archaeological) situate all the samples in the last phase of the Holocene. In the three deposits Pinus pinaster has been identified and there have being made other taxonomic contributions. A review of previous Pinus pinaster findings registered in the Peninsula is exposed and other considerations are made on the importance of this taxon in the Iberian vegetal landscape during the end of the Quaternary.Se ha realizado un estudio de restos fósiles leñosos correspondientes a tres yacimientos del interior de la península Ibérica: Hontalbilla (Segovia), Yecla (Murcia) y Castillejos (Badajoz). Las cronologías establecidas mediante datación absoluta (radiocarbono) o relativa (arqueológica) sitúan todas las muestras en la última fase del Holoceno. En los tres yacimientos se ha identificado Pinus pinaster, realizándose además otras aportaciones taxonómicas. Se reúnen los datos conocidos de macrorrestos de P. pinaster registrados en la Península y se realizan consideraciones sobre la importancia de este taxon en el paisaje vegetal ibérico durante el final del Cuaternario

Marine spatial planning and Good Environmental status: A perspective on spatial and temporal dimensions

The European Union Marine Strategy Framework Directive requires the Good Environmental Status of marine environments in Europe's regional seas; yet, maritime activities, including sources of marine degradation, are diversifying and intensifying in an increasingly globalized world. Marine spatial planning is emerging as a tool for rationalizing competing uses of the marine environment while guarding its quality. A directive guiding the development of such plans by European Union member states is currently being formulated. There is an undeniable need for marine spatial planning. However, we argue that considerable care must be taken with marine spatial planning, as the spatial and temporal scales of maritime activities and of Good Environmental Status may be mismatched. We identify four principles for careful and explicit consideration to align the requirements of the two directives and enable marine spatial planning to support the achievement of Good Environmental Status in Europe's regional seas