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

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Brazilian gutta-percha points: Part I: chemical composition and X-ray diffraction analysis

Eight nonstandardized gutta-percha points commercially available in Brazil (Konne, Tanari, Endopoint, Odous, Dentsply 0.04, Dentsply 0.06, Dentsply TP and Dentsply FM) were analysed chemically and by X-ray diffraction, and their chemical compositions were compared. The organic fraction (gutta-percha polymer and wax/resin) of the gutta-percha points was separated from the inorganic fraction (ZnO and BaSO4) by dissolving them in chloroform. The gutta-percha polymer was precipitated with acetone. The inorganic fraction was analysed by elemental microanalysis. Energy-dispersive X-ray microanalysis (EDX) and X-ray diffraction were employed to identify the chemical elements and compounds (barium sulfate and zinc oxide). The barium sulfate content was calculated based on the percentage of sulfur found in the elemental microanalysis. All analyses were repeated three times. The means and standard deviations of the percentage by weight of gutta-percha in the points were: Konne (17.6 ± 0.30), Tanari (15.2 ± 0.30), Endopoint (16.7 ± 0.23), Odous (18.8 ± 0.20), Dentsply 0.04 (15.7 ± 0.17), Dentsply 0.06 (16.6 ± 0.17), Dentsply TP (21.6 ± 0.15) and Dentsply FM (16.3 ± 0.23). The means and standard deviations of the zinc oxide content were: Konne (79.9 ± 0.10), Tanari (81.9 ± 0.07), Endopoint (81.3 ± 0.40), Odous (79.7 ± 0.26), Dentsply 0.04 (77.9 ± 0.03), Dentsply 0.06 (78.2 ± 0.07), Dentsply TP (69.8 ± 0.19) and Dentsply FM (72.6 ± 0.70). The method utilized was appropriate to quantify gutta-percha, wax/resin, zinc oxide and barium sulfate. Cone brands without barium sulfate were found. An unusual high wax/resin percentage was detected in Dentsply FM (p = 0.0003). Dentsply TP showed the highest gutta-percha percentage