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,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

Viability of gait speed test in hospitalized elderly patients

ABSTRACT Objective: The gait speed test (GST) is a physical test that can predict falls and aid in the diagnosis of sarcopenia in the elderly. However, to our knowledge, there have been no studies evaluating its reproducibility in hospitalized elderly patients. The objective of this study was to evaluate the safety and reproducibility of the six-meter GST (6GST) in hospitalized elderly patients. Methods: This repeated measures study involved hospitalized elderly patients (≥ 60 years of age) who underwent the 6GST by the fifth day of hospitalization, were able to walk without assistance, and presented no signs of dyspnea or pain that would prevent them from performing the test. The 6GST was performed three times in sequence, with a rest period between each test, in a level corridor. Gait speed was measured in meters/second. Reproducibility was assessed by comparing the means, intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results: We evaluated 110 elderly patients in a total of 330 tests. All participants completed all of the tests. The comparisons between the speeds obtained during the three tests showed high ICCs and a low mean bias (Bland-Altman plots). The correlation and accuracy were greatest when the mean maximum speed was compared with that obtained in the third test (1.26 ± 0.44 m/s vs. 1.22 ± 0.44 m/s; ICC = 0.99; p = 0.001; mean bias = 0.04; and limits of agreement = −0.27 to 0.15). Conclusions: The 6GST was proven to be safe and to have good reproducibility in this sample of hospitalized elderly patients. The third measurement seems to correspond to the maximum speed, since the first two measurements underestimated the actual performance

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

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