941 resultados para saturation number


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O teste de caminhada em seis minutos (TC6M) avalia a capacidade respiratória durante o exercício. Recentemente, o teste do degrau em seis minutos (TD6M) está sendo estudado como uma proposta para essa mesma avaliação. Diante do exposto, o desenvolvimento de uma equação de referência se torna importante. O objetivo desse estudo foi desenvolver uma equação de referência padrão para o Teste do Degrau em Seis minutos. Esse estudo foi do tipo transversal, em que foram selecionados 452 indivíduos. Após a aplicação dos critérios de inclusão/exclusão, foram selecionados 326 sujeitos saudáveis e sedentários com idade entre 20 e 80 anos. Para serem considerados saudáveis, os participantes não podiam ter história de doenças (exceto hipertensão arterial sistêmica ou diabetes mellitus em tratamento) e foram submetidos à radiografia de tórax, espirometria e eletrocardiograma, que deveriam ser normais. O nível de sedentarismo foi avaliado através do International Physical Activity Questionnaire (IPAQ). Foram coletados os seguintes dados demográficos: idade, peso e altura. Todos os indivíduos realizaram TD6M na sua própria cadência (autocadenciado) em um degrau de 16,5 cm de altura, 65 de largura e 30 cm de comprimento. O número de subidas e descidas foi contado por um pedômetro digital. Foram mensuradas a pressão arterial, a frequência cardíaca e a saturação de oxigênio, antes e depois do TD6M. A análise estatística foi realizada pelo software STATA 12.0, e as equações foram desenvolvidas pelo modelo estatístico de regressão linear múltipla. Como resultado, observou-se que os participantes apresentaram exames normais, sendo 135 homens e 191 mulheres. O IPAQ demonstrou 157 ativos, 114 irregularmente ativos e 40 inativos, sendo que 14 indivíduos não responderam. A análise da distância alcançada em relação à idade e à diferença da frequência cardíaca tanto para homens quanto para mulheres mostrou significância estatística, demonstrando a sua importância para o desenvolvimento das equações para cada gênero. Para mulheres: Distância (m) = 88,83 - [(FC final- FC inicial)* 0,23] (Idade *0,37); para homens: Distância (m)= 110,20 - [(FC final- FC inicial)*0,18] (Idade * 0,59). O estudo concluiu que as equações de referência desenvolvidas nesse estudo foram realizadas em uma população de indivíduos saudáveis e sedentários e pode ser usada como padrão de referência para o TD6M.

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Catch rates from fishery-independent surveys often are assumed to vary in proportion to the actual abundance of a population, but this approach assumes that the catchability coefficient (q) is constant. When fish accumulate in a gear, the rate at which the gear catches fish can decline, and, as a result, catch asymptotes and q declines with longer fishing times. We used data from long-term trap surveys (1990–2011) in the southeastern U.S. Atlantic to determine whether traps saturated for 8 reef fish species because of the amount of time traps soaked or the level of fish accumulation (the total number of individuals of all fish species caught in a trap). We used a delta-generalized-additive model to relate the catch of each species to a variety of predictor variables to determine how catch was influenced by soak time and fish accumulation after accounting for variability in catch due to the other predictor variables in the model. We found evidence of trap saturation for all 8 reef fish species examined. Traps became saturated for most species across the range of soak times examined, but trap saturation occurred for 3 fish species because of fish accumulation levels in the trap. Our results indicate that, to infer relative abundance levels from catch data, future studies should standardize catch or catch rates with nonlinear regression models that incorporate soak time, fish accumulation, and any other predictor variable that may ultimately influence catch. Determination of the exact mechanisms that cause trap saturation is a critical need for accurate stock assessment, and our results indicate that these mechanisms may vary considerably among species.

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The primary objective of this study was to predict the distribution of mesophotic hard corals in the Au‘au Channel in the Main Hawaiian Islands (MHI). Mesophotic hard corals are light-dependent corals adapted to the low light conditions at approximately 30 to 150 m in depth. Several physical factors potentially influence their spatial distribution, including aragonite saturation, alkalinity, pH, currents, water temperature, hard substrate availability and the availability of light at depth. Mesophotic corals and mesophotic coral ecosystems (MCEs) have increasingly been the subject of scientific study because they are being threatened by a growing number of anthropogenic stressors. They are the focus of this spatial modeling effort because the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS) is exploring the expansion of its scope—beyond the protection of the North Pacific Humpback Whale (Megaptera novaeangliae)—to include the conservation and management of these ecosystem components. The present study helps to address this need by examining the distribution of mesophotic corals in the Au‘au Channel region. This area is located between the islands of Maui, Lanai, Molokai and Kahoolawe, and includes parts of the Kealaikahiki, Alalākeiki and Kalohi Channels. It is unique, not only in terms of its geology, but also in terms of its physical oceanography and local weather patterns. Several physical conditions make it an ideal place for mesophotic hard corals, including consistently good water quality and clarity because it is flushed by tidal currents semi-diurnally; it has low amounts of rainfall and sediment run-off from the nearby land; and it is largely protected from seasonally strong wind and wave energy. Combined, these oceanographic and weather conditions create patches of comparatively warm, calm, clear waters that remain relatively stable through time. Freely available Maximum Entropy modeling software (MaxEnt 3.3.3e) was used to create four separate maps of predicted habitat suitability for: (1) all mesophotic hard corals combined, (2) Leptoseris, (3) Montipora and (4) Porites genera. MaxEnt works by analyzing the distribution of environmental variables where species are present, so it can find other areas that meet all of the same environmental constraints. Several steps (Figure 0.1) were required to produce and validate four ensemble predictive models (i.e., models with 10 replicates each). Approximately 2,000 georeferenced records containing information about mesophotic coral occurrence and 34 environmental predictors describing the seafloor’s depth, vertical structure, available light, surface temperature, currents and distance from shoreline at three spatial scales were used to train MaxEnt. Fifty percent of the 1,989 records were randomly chosen and set aside to assess each model replicate’s performance using Receiver Operating Characteristic (ROC), Area Under the Curve (AUC) values. An additional 1,646 records were also randomly chosen and set aside to independently assess the predictive accuracy of the four ensemble models. Suitability thresholds for these models (denoting where corals were predicted to be present/absent) were chosen by finding where the maximum number of correctly predicted presence and absence records intersected on each ROC curve. Permutation importance and jackknife analysis were used to quantify the contribution of each environmental variable to the four ensemble models.

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