Sedentary subjects have higher PAI-1 and lipoproteins levels than highly trained athletes

Physical exercise protects against the development of cardiovascular disease, partly by lowering plasmatic total cholesterol, LDL-cholesterol and increased HDL-cholesterol levels. In addition, it is now established that reduction plasmatic adiponectin and increased C-reactive protein (CRP) and plasminogen activator inhibitor-1 (PAI-1) levels play a role in the maintenance of an inflammatory state and in the development of cardiovascular disease. This study aimed to examine plasma lipid profile and inflammatory markers levels in individual with sedentary lifestyle and/or highly trained athletes at rest. Methods: Fourteen male subjects (sedentary lifestyle n = 7 and highly trained athletes n = 7) were recruited. Blood samples were collected after an overnight fast (~12 h). The plasmatic lipid profile (Triglycerides, HDL-cholesterol, LDL-cholesterol, total cholesterol, LDL-oxidized and total cholesterol/HDL-c ratio), glucose, adiponectin, C - reactive protein and PAI-1 levels were determined. Results: Total cholesterol, LDL-cholesterol, TG and PAI-1 levels were lower in highly trained athletes group in relation to sedentary subjects (p < 0.01). In addition, we observed a positive correlation between PAI-1 and total cholesterol (r = 0.78; p < 0.0009), PAI-1 and LDL-c (r = 0.69; p < 0.006) and PAI-1 and TG levels (r = 0.56; p < 0.03). The plasma concentration of adiponectin, CRP, glucose, HDL-cholesterol and total cholesterol/HDL-c ratio levels were not different. These results indicate that lifestyle associated with high intensity and high volume exercise induces changes favourable in the lipid profile and PAI-1 levels and may reduce risk cardiovascular diseases

Rapid assessment survey for exotic benthic species in the São Sebastião Channel, Brazil

The study of biological invasions can be roughly divided into three parts: detection, monitoring, mitigation. Here, our objectives were to describe the marine fauna of the area of the port of São Sebastião (on the northern coast of the state of São Paulo, in the São Sebastião Channel, SSC) to detect introduced species. Descriptions of the faunal community of the SSC with respect to native and allochthonous (invasive or potentially so) diversity are lacking for all invertebrate groups. Sampling was carried out by specialists within each taxonomic group, in December 2009, following the protocol of the Rapid Assessment Survey (RAS) in three areas with artificial structures as substrates. A total of 142 species were identified (61 native, 15 introduced, 62 cryptogenic, 4 not classified), of which 17 were Polychaeta (12, 1, 1, 3), 24 Ascidiacea (3, 6, 15, 0), 36 Bryozoa (17, 0, 18, 1), 27 Cmdana (2, 1, 24, 0), 20 Crustacea (11, 4, 5, 0), 2 Entoprocta (native), 16 Mollusca (13, 3, 0, 0). Twelve species are new occurrences for the SSC. Among the introduced taxa, two are new for coastal Brazil. Estimates of introduced taxa are conservative as the results of molecular studies suggest that some species previously considered cryptogenic are indeed introduced. We emphasize that the large number of cryptogenic species illustrates the need for a long-term monitoring program, especially in areas most susceptible to bioinvasion. We conclude that rapid assessment studies, even in relatively well-known regions, can be very useful for the detection of introduced species and we recommend that they be carried out on a larger scale in all ports with heavy ship traffic.Center of Marine Biology of the University of São Paulolhabela Yacht ClubCAPES-PROCAD 2007/150FAPESP (2004/09961-4; 2006/58226-0; 2010/06927-0)CAPES (Pró-Equipamentos and Prodoc projects)Boticário FoundationCNPqCAPESFAPESP (2008/10619-0)PNPD/CAPESFACEPE (BCT 0039-1.08/10)NP-BioMar, USPSpecial Issue: “Proceedings of the 3rd Brazilian Congress of Marine Biology”. A.C. Marques, L.V.C. Lotufo, P.C. Paiva, P.T.C. Chaves & S.N. Leitão (Guest Editors

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost