31 research outputs found
Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
The new method is proposed to monitor the level of current physical load and
accumulated fatigue by several objective and subjective characteristics. It was
applied to the dataset targeted to estimate the physical load and fatigue by
several statistical and machine learning methods. The data from peripheral
sensors (accelerometer, GPS, gyroscope, magnetometer) and brain-computing
interface (electroencephalography) were collected, integrated, and analyzed by
several statistical and machine learning methods (moment analysis, cluster
analysis, principal component analysis, etc.). The hypothesis 1 was presented
and proved that physical activity can be classified not only by objective
parameters, but by subjective parameters also. The hypothesis 2 (experienced
physical load and subsequent restoration as fatigue level can be estimated
quantitatively and distinctive patterns can be recognized) was presented and
some ways to prove it were demonstrated. Several "physical load" and "fatigue"
metrics were proposed. The results presented allow to extend application of the
machine learning methods for characterization of complex human activity
patterns (for example, to estimate their actual physical load and fatigue, and
give cautions and advice).Comment: 12 pages, 10 figures, 1 table; presented at XXIX IUPAP Conference in
Computational Physics (CCP2017) July 9-13, 2017, Paris, University Pierre et
Marie Curie - Sorbonne (https://ccp2017.sciencesconf.org/program
Quadratic Time-dependent Quantum Harmonic Oscillator
We present a Lie algebraic approach to a Hamiltonian class covering driven,
parametric quantum harmonic oscillators where the parameter set -- mass,
frequency, driving strength, and parametric pumping -- is time-dependent. Our
unitary-transformation-based approach provides a solution to the general
quadratic time-dependent quantum harmonic model. As an example, we show an
analytic solution to the periodically driven quantum harmonic oscillator
without the rotating wave approximation; it works for any given detuning and
coupling strength regime. For the sake of completeness, we provide an analytic
solution to the historical Caldirola--Kanai quantum harmonic oscillator that,
in a suitable reference frame, is just a time-independent parametric quantum
harmonic oscillator.Comment: 22 pages, 4 figure
Contribución de los heterótrofos a la calcificación secundaria en arrecifes marginales del Pacífico mexicano
Background. Sclerobionts (e.g., calcareous algae, bryozoans, polychaetes, mollusks, and barnacles) produce reef calcium carbonate (CaCO3). Their contribution is key to maintaining positive CaCO3 balances, especially in marginal reefs. Objective. To compare the production of CaCO3 by sclerobionts in two marginal reefs of the Mexican Pacific: Las Gatas (LG), in Zihuatanejo Guerrero Bay, and La Llave (LL), in Bahía de Los Angeles (Gulf of California). Methods. CAUs (Calcification/Accretion Units) were used to promote sclerobiont recruitment during two deployment times: 6 and 15 months. Results. The calcification rate was high at six months and then decreased due to rapid colonization and initial growth, followed by a decrease over time. Sclerobionts deposited 1.2 ± 0.4 kg CaCO3 m-2 yr-1 in LG, which represents 7% of the production of branching corals in the Mexican South Pacific (17.2 kg m-2 yr-1), while in LL, they deposited 2.1 ± 0.7 kg CaCO3 m-2 yr-1; equivalent to 20% of the production of massive corals in the area (10.1 kg m-2 yr-1). The groups that deposited most CaCO3were mollusks and bryozoans in LG (up to 0.65 ± 0.16 kg m-2 yr-1) and barnacles in LL (up to 2.32 ± 0.35 kg m-2 yr-1). Conclusions. These results highlight the role of heterotrophs as secondary calcifiers both in LG, a site impacted by anthropogenic activity, and in LL, an area with low anthropogenic impact but high biological productivity associated with upwellings. This finding implies that the environmental conditions at the study sites limit the calcification of primary calcifiers (i.e., corals and CCA) but promote that of secondary calcifiers, with potential geomorphic repercussionsAntecedentes. Los esclerobiontes (e.g., algas calcáreas, briozoos, poliquetos, moluscos, y balanos) par-ticipan en la producción de carbonato de calcio (CaCO3) arrecifal. Su contribución es vital para mantener balances de CaCO3 positivos, especialmente en arrecifes marginales. Objetivo. Comparar la producción de CaCO3 por esclerobiontes en un arrecife sujeto a estrés antropogénico - Las Gatas (LG), en la bahía de Zihua-tanejo Guerrero, y otro bajo condiciones ambientales altamente fluctuantes - La Llave (LL), en Bahía de Los Ángeles (Golfo de California). Métodos. Se utilizaron CAUs (Calcification/Accretion Units) para promover el reclutamiento de esclerobiontes durante dos periodos de inmersión: 6 y 15 meses. Resultados. La tasa de calcificación fue alta a los 6 meses y luego disminuyó debido a la rápida colonización y crecimiento inicial seguido de una disminución con el tiempo. Los esclerobiontes depositaron 1.2 ± 0.4 kg CaCO3 m-2 año-1 en LG, lo que representa el 7% de la producción de corales ramificados en el Pacífico sur mexicano (17.2 kg m-2año-1), mientras que en LL depositaron 2.1 ± 0.7 kg CaCO3 m-2 año-1, equivalente al 20% de la producción de corales masivos en la zona (10.1 kg m-2 año-1). Los grupos que más CaCO3 depositaronfueron los moluscos y los briozoos en LG (hasta 0.65 ± 0.16 kg m-2 año-1), y los balanos en LL (hasta 2.32 ± 0.35 kg m-2 año-1). Conclusiones. Estos resultados destacan el papel de los heterótrofos como calcificadores secundarios tanto en LG, un sitio impactado por actividad antropogénica, como en LL, un sitio con bajo impacto antropogénico, pero con alta productividad biológica asociada a surgencias. Este hallazgo implica que las condiciones am-bientales en los sitios de estudio limitan la calcificación de los calcificadores primarios (i.e., corales y CCA) pero estimulan la de calcificadores secundarios, con potenciales repercusiones geomórficas
Rediscovering Kemp’s Ridley Sea Turtle (<em>Lepidochelys kempii</em>): Molecular Analysis and Threats
Sea turtles are reptiles that have inhabited the earth for 100 million years. These are divided into 2 families (Cheloniidae and Dermochelyidae) and 7 species of sea turtles in the world: the leatherback turtle (Dermochelys coriacea); hawksbill turtle (Eretmochelys imbricata); Kemp’s ridley (Lepidochelys kempii); olive ridley (L. olivacea); Loggerhead turtle (Caretta caretta); flatback sea turtle (Natator depressus) and green turtle (Chelonia mydas). In particular, Kemp’s ridley is included in the red list of IUCN categorized as “critically endangered”. The most important site around the Word is in Rancho Nuevo, Tamaulipas, Mexico. Where 80–95% of the world’s nesting is concentrated. Other nesting areas are Tepeguajes and Barra del Tordo, in Tamaulipas, and with less intensity in Veracruz (Lechuguillas and El Raudal beaches) and South Padre Island, Texas, USA. They deposit an average of about 90 eggs and hatching takes 40 to 60 days. Therefore, they are vulnerable to different anthropogenic activities and sources of pollution, such as heavy metals, which can cause toxic effects that are harmful to the turtles, damage their physiology and health. To understand the real situation about health and genetic parameters it is necessary to analyze biochemical and molecular factors in this species
Revista de Vertebrados de la Estación Biológica de Doñana
Descripción de una nueva subespecie de lagarto ágil (Lacerta agilis garzoni) de los PirineosDiet of the Montagu's Harrier (Circus pygargus) in southwestern Spain SpainObservaciones ornitológicas en la Guayana francesaDaily feeding rhythm of ducks on the marismas of the Guadalquivir and their responses to birds of preyA note on the emetic technique for obtaining food samples from passerine birds.Distribución de contaminantes organoclorados en tejidos de garza imperial (Ardea purpurea) y pato cuchara (Anas clypeata) de la Reserva Biológica de Doñana.Etograma cuantificado del gamo (Dama dama) en libertad.Peer reviewe
Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization
This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are available in the Supplementary Material of this article and Zenodo (https://doi.org/10.5281/zenodo.5898578). Details for all animals included in this study are provided in Appendices S1 and S2. Data used to create the spatial networks are listed in the Appendices S3 and S4. The geospatial files for all networks are available on the Migratory Connectivity in the Ocean Project website (https://mico.eco) and Dryad (https://doi.org/10.5061/dryad.j3tx95xg9). Additional data that support the findings of this study are available from the corresponding author upon reasonable request.Aim
Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts.
Location
Global.
Methods
We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections.
Results
Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links.
Main conclusions
Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.International Climate Initiative (IKI)German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU
Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization
Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global. Methods: We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.Fil: Kot, Connie Y.. University of Duke; Estados UnidosFil: Åkesson, Susanne. Lund University; SueciaFil: Alfaro Shigueto, Joanna. Universidad Cientifica del Sur; Perú. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Amorocho Llanos, Diego Fernando. Research Center for Environmental Management and Development; ColombiaFil: Antonopoulou, Marina. Emirates Wildlife Society-world Wide Fund For Nature; Emiratos Arabes UnidosFil: Balazs, George H.. Noaa Fisheries Service; Estados UnidosFil: Baverstock, Warren R.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Blumenthal, Janice M.. Cayman Islands Government; Islas CaimánFil: Broderick, Annette C.. University of Exeter; Reino UnidoFil: Bruno, Ignacio. Instituto Nacional de Investigaciones y Desarrollo Pesquero; ArgentinaFil: Canbolat, Ali Fuat. Hacettepe Üniversitesi; Turquía. Ecological Research Society; TurquíaFil: Casale, Paolo. Università degli Studi di Pisa; ItaliaFil: Cejudo, Daniel. Universidad de Las Palmas de Gran Canaria; EspañaFil: Coyne, Michael S.. Seaturtle.org; Estados UnidosFil: Curtice, Corrie. University of Duke; Estados UnidosFil: DeLand, Sarah. University of Duke; Estados UnidosFil: DiMatteo, Andrew. CheloniData; Estados UnidosFil: Dodge, Kara. New England Aquarium; Estados UnidosFil: Dunn, Daniel C.. University of Queensland; Australia. The University of Queensland; Australia. University of Duke; Estados UnidosFil: Esteban, Nicole. Swansea University; Reino UnidoFil: Formia, Angela. Wildlife Conservation Society; Estados UnidosFil: Fuentes, Mariana M. P. B.. Florida State University; Estados UnidosFil: Fujioka, Ei. University of Duke; Estados UnidosFil: Garnier, Julie. The Zoological Society of London; Reino UnidoFil: Godfrey, Matthew H.. North Carolina Wildlife Resources Commission; Estados UnidosFil: Godley, Brendan J.. University of Exeter; Reino UnidoFil: González Carman, Victoria. Instituto National de Investigación y Desarrollo Pesquero; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Harrison, Autumn Lynn. Smithsonian Institution; Estados UnidosFil: Hart, Catherine E.. Grupo Tortuguero de las Californias A.C; México. Investigacion, Capacitacion y Soluciones Ambientales y Sociales A.C; MéxicoFil: Hawkes, Lucy A.. University of Exeter; Reino UnidoFil: Hays, Graeme C.. Deakin University; AustraliaFil: Hill, Nicholas. The Zoological Society of London; Reino UnidoFil: Hochscheid, Sandra. Stazione Zoologica Anton Dohrn; ItaliaFil: Kaska, Yakup. Dekamer—Sea Turtle Rescue Center; Turquía. Pamukkale Üniversitesi; TurquíaFil: Levy, Yaniv. University Of Haifa; Israel. Israel Nature And Parks Authority; IsraelFil: Ley Quiñónez, César P.. Instituto Politécnico Nacional; MéxicoFil: Lockhart, Gwen G.. Virginia Aquarium Marine Science Foundation; Estados Unidos. Naval Facilities Engineering Command; Estados UnidosFil: López-Mendilaharsu, Milagros. Projeto TAMAR; BrasilFil: Luschi, Paolo. Università degli Studi di Pisa; ItaliaFil: Mangel, Jeffrey C.. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Margaritoulis, Dimitris. Archelon; GreciaFil: Maxwell, Sara M.. University of Washington; Estados UnidosFil: McClellan, Catherine M.. University of Duke; Estados UnidosFil: Metcalfe, Kristian. University of Exeter; Reino UnidoFil: Mingozzi, Antonio. Università Della Calabria; ItaliaFil: Moncada, Felix G.. Centro de Investigaciones Pesqueras; CubaFil: Nichols, Wallace J.. California Academy Of Sciences; Estados Unidos. Center For The Blue Economy And International Environmental Policy Program; Estados UnidosFil: Parker, Denise M.. Noaa Fisheries Service; Estados UnidosFil: Patel, Samir H.. Coonamessett Farm Foundation; Estados Unidos. Drexel University; Estados UnidosFil: Pilcher, Nicolas J.. Marine Research Foundation; MalasiaFil: Poulin, Sarah. University of Duke; Estados UnidosFil: Read, Andrew J.. Duke University Marine Laboratory; Estados UnidosFil: Rees, ALan F.. University of Exeter; Reino Unido. Archelon; GreciaFil: Robinson, David P.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Robinson, Nathan J.. Fundación Oceanogràfic; EspañaFil: Sandoval-Lugo, Alejandra G.. Instituto Politécnico Nacional; MéxicoFil: Schofield, Gail. Queen Mary University of London; Reino UnidoFil: Seminoff, Jeffrey A.. Noaa National Marine Fisheries Service Southwest Regional Office; Estados UnidosFil: Seney, Erin E.. University Of Central Florida; Estados UnidosFil: Snape, Robin T. E.. University of Exeter; Reino UnidoFil: Sözbilen, Dogan. Dekamer—sea Turtle Rescue Center; Turquía. Pamukkale University; TurquíaFil: Tomás, Jesús. Institut Cavanilles de Biodiversitat I Biologia Evolutiva; EspañaFil: Varo Cruz, Nuria. Universidad de Las Palmas de Gran Canaria; España. Ads Biodiversidad; España. Instituto Canario de Ciencias Marinas; EspañaFil: Wallace, Bryan P.. University of Duke; Estados Unidos. Ecolibrium, Inc.; Estados UnidosFil: Wildermann, Natalie E.. Texas A&M University; Estados UnidosFil: Witt, Matthew J.. University of Exeter; Reino UnidoFil: Zavala Norzagaray, Alan A.. Instituto politecnico nacional; MéxicoFil: Halpin, Patrick N.. University of Duke; Estados Unido
Isolation, Characterization, and Antibiotic Resistance of Vibrio spp. in Sea Turtles from Northwestern Mexico
The aerobic oral and cloacal bacterial microbiota and their antimicrobial resistance were characterized for 64 apparently healthy sea turtles captured at their foraging grounds in Ojo de Liebre Lagoon (OLL), Baja California Sur, Mexico (Pacific Ocean) and the lagoon system of Navachiste (LSN) and Marine Area of Influence (MAI), Guasave, Sinaloa (Gulf of California). A total of 34 black turtles (Chelonia mydas agassizii) were sampled in OLL and eight black turtles and 22 olive ridley turtles (Lepidochelys olivacea) were sampled in LSN and MAI, respectively from January to December 2012. We isolated 13 different species of Gram-negative bacteria. The most frequently isolated bacteria were Vibrio alginolyticus in 39/64 (60%), V. parahaemolyticus in 17/64 (26%) and V. cholerae in 6/64 (9%,). However, V. cholerae was isolated only from turtles captured from the Gulf of California (MAI). Among V. parahaemolyticus strains, six O serogroups and eight serovars were identified from which 5/17 (29.4%) belonged to the pathogenic strains (tdh+ gene) and 2/17 (11.7%) had the pandemic clone (tdh+ and toxRS/new+). Among V. cholerae strains, all were identified as non-O1/non-O139, and in 4/6 (66%) the accessory cholera enterotoxin gene (ace) was identified but without virulence gene zot, ctxA and ctxB. Of the isolated V. parahaemolyticus, V. cholerae and V. alginolyticus strains, 94.1%, 33.4% and 100% demonstrated resistance to at least one commonly prescribed antibiotic (primarily to ampicillin), respectively. In conclusion, the presence of several potential (toxigenic) human pathogens in sea turtles may represent transmission of environmental microbes and a high-risk of food-borne disease. Therefore, based on the fact that it is illegal and unhealthy, we discourage the consumption of sea turtle meat or eggs in northwestern Mexico
Trace elements in blood of sea turtles lepidochelys olivacea in the gulf of California, Mexico
This study determined the concentrations of heavy metals in blood collected from Pacific Ridley sea turtles (Lepidochelys olivacea) inhabiting the coast of Guasave, Mexico, in the Gulf of California. The highest reported metal concentration in blood was Zn, followed by Se. Of nonessential toxic metals, As was reported in higher percentage compared to Cd. The concentrations of metals detected were present as follows: Zn > Se > Mn > As > Ni > Cd > Cu. Cd concentration in blood is higher in our population in comparison with other populations of L. olivacea, and even higher in other species of sea turtles. Our study reinforces the usefulness of blood for the monitoring of the levels of contaminating elements, and is easily accessible and nonlethal for sea turtles. � 2014 Springer Science+Business Media New York