POTENTIAL EFFECTS OF WHOLE-BODY VIBRATION EXERCISES ON BLOOD FLOW KINETICS OF DIFFERENT POPULATIONS: A SYSTEMATIC REVIEW WITH A SUITABLE APPROACH

Background: The ability to control skin blood flow decreases with advancing age and some clinical disorders, as in diabetes and in rheumatologic diseases. Feasible clinical strategies such as whole-body vibration exercise (WBVE) are being used without a clear understanding of its effects. The aim of the present study is to review the effects of the WBVE on blood flow kinetics and its feasibility in different populations. Material and Methods: The level of evidence (LE) of selected papers in PubMed and/or PEDRo databases was determined. We selected randomized, controlled trials in English to be evaluated. Results: Six studies had LE II, one had LE III-2 and one III-3 according to the NHMRC. A great variability among the protocols was observed but also in the assessment devices; therefore, more research about this topic is warranted. Conclusion: Despite the limitations, it is can be concluded that the use of WBVE has proven to be a safe and useful strategy to improve blood flow. However, more studies with greater methodological quality are needed to clearly define the more suitable protocols

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Distribuição, probabilidade de ocorrência e período de retorno dos índices de erosividade EI30 e KE>25 em Seropédica - RJ Erosivity indexes EI30 and KE>25 at Seropedica, Rio de Janeiro State, Brazil: distribution, occurrence probability and return period

O presente trabalho foi desenvolvido com o objetivo de estudar as características da erosividade da chuva em Seropédica (RJ), quanto à sua distribuição, probabilidade de ocorrência e período de retorno. Para isso, foi utilizada uma série mensal de dados pluviométricos referente ao período de 1973 a 2002 e, com o auxílio de modelos ajustados para a região, foi possível obter os índices mensais e anuais de erosividade EI30 e KE>25. Com base nos resultados obtidos, foi possível concluir que: a) os valores médios anuais de EI30 e de KE>25 foram de 5.960,4 MJ mm ha-1 h-1 e de 99,2 MJ ha-1, respectivamente, e estão associados a períodos de retorno de 1,97 ano, com uma probabilidade de ocorrência de 50,82%; e b) valores anuais de EI30 da ordem de 5.995; 7.262; 7.684; 7.895; 8.022 e 8.064 MJ mm ha-1 h-1 e de KE>25 da ordem de 99,8; 122,7; 130,3; 134,1; 136,4 e 137,1 MJ ha-1, são esperados, em média, uma vez a cada 2; 5; 10; 20; 50 e 100 anos, respectivamente.<br>This work was carried out in order to study the distribution, the occurrence probability and the return period of the rainfall erosivity in Seropédica city, Rio de Janeiro State, Brazil. It was considered a continuous rain gauges series for the period from 1973 to 2002, and by using specific adjustment models for the region, it was determined the annual and monthly erosivity indexes EI30 and KE>25. With the obtained results, it was possible to conclude that: a) the annual erosivity indexes values EI30 and KE>25 were 5,960.4 MJ mm ha-1 and 99.2 MJ ha-1 , respectively, which was expected to occur at least once every 1.97 year, with an occurrence probability of 50.82%; and b) annual values EI30 of 5,995; 7,262; 7,684; 7,895 8,022 and8,064 MJ mm ha-1 and annual values KE>25 of 99.8; 122.7; 130.3; 134.1; 136.4 and 137.1 MJ ha-1 are expected at least once every 2; 5; 10; 20; 50 and 100 years, respectively