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

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

Baseline heavy metals and metalloid values in blood of loggerhead turtles (Caretta caretta) from Baja California Sur, Mexico

Environmental pollution due to heavy metals is having an increased impact on marine wildlife accentuated by anthropogenic changes in the planet including overfishing, agricultural runoff and marine emerging infectious diseases. Sea turtles are considered sentinels of ecological health in marine ecosystems. The objective of this study was to determine baseline concentrations of zinc, cadmium, copper, nickel, selenium, manganese, mercury and lead in blood of 22 clinically healthy, loggerhead turtles (Caretta caretta), captured for several reasons in Puerto López Mateos, Baja California Sur, Mexico. Zinc was the most prevalent metal in blood (41.89 lg g!1), followed by Selenium (10.92 lg g!1). The mean concentration of toxic metal Cadmium was 6.12 lg g!1 and 1.01 lg g!1 respectively. Mean concentrations of metals followed this pattern: Zn > Se > Ni > Cu > Mn > Cd > Pb and Hg. We can conclude that blood is an excellent tissue to measure in relatively non-invasive way baseline values of heavy metals in Caretta caretta

Morphometric and blood biochemistry values of olive ridley turtles (<i>Lepidochelys olivacea</i>) from a foraging aggregation in northern Sinaloa, Mexico by year of capture, 2013–2015.

<p>Morphometric and blood biochemistry values of olive ridley turtles (<i>Lepidochelys olivacea</i>) from a foraging aggregation in northern Sinaloa, Mexico by year of capture, 2013–2015.</p

Morphometric and blood biochemistry values of olive ridley turtles (<i>Lepidochelys olivacea</i>) from a foraging aggregation in northern Sinaloa, Mexico, 2013–2015.

<p>Morphometric and blood biochemistry values of olive ridley turtles (<i>Lepidochelys olivacea</i>) from a foraging aggregation in northern Sinaloa, Mexico, 2013–2015.</p

Morphometric and blood biochemistry values of adult and subadult olive ridley turtles (<i>Lepidochelys olivacea</i>) from a foraging aggregation in northern Sinaloa, Mexico, 2013–2015.

<p>Morphometric and blood biochemistry values of adult and subadult olive ridley turtles (<i>Lepidochelys olivacea</i>) from a foraging aggregation in northern Sinaloa, Mexico, 2013–2015.</p

Mercury-Induced Chromosomal Damage in Wild Fish (Dicentrarchus labrax L.) Reflecting Aquatic Contamination in Contrasting Seasons

Ria de Aveiro (mainly Laranjo basin, Portugal) has been subjected to mercury contamination from a chlor-alkali plant, currently presenting a well-described mercury gradient. This study aimed to assess mercury genotoxicity in this area by measuring the frequency of erythrocytic nuclear abnormalities (ENA) in the European sea bass (Dicentrarchus labrax), addressing the relation with total mercury concentration in the blood and the modulatory role of seasonal variables. Fish were collected, in warm and cold periods, at three locations differing in their distances to the main mercury source: reference (R), moderately (M), and highly (H) contaminated sites. Genotoxicity was detected in both degrees of contamination (M and H) and in both periods of the year (warm and cold), which is in line with the greater levels of mercury measured in fish blood. No significant seasonal variations were observed for mercury bioaccumulation or ENA frequency. The apparent low imperviousness of ENA frequency to seasonal factors reinforced its consistency as a genotoxicity biomarker, thus enabling a clearer identification of cause-and-effect relationships. Overall, the results reflected a serious environmental risk to native ichthyofauna at Laranjo basin due to mercury contamination, showing a potential of mercury to induce genetic damage in fish blood cells through clastogenic and/or aneugenic actions