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

Staphylococcus aureus-Cure-Associated Antigens Elicit Type 3 Immune Memory T Cells

Background: Staphylococcus aureus is one of the most frequently major mastitis pathogens that cause clinical and subclinical mastitis worldwide. Current antimicrobial treatments are usually ineffective, and the commercially available vaccines lack proven effectiveness. The immunological response elicited by the recombinant S. aureus-cure-associated proteins phosphoglycerate kinase (PGK), enolase (ENO), and elongation factor-G (EF-G) in combination with the granulocyte-macrophage colony-stimulating factor (GM-CSF) DNA vaccination was studied in this work. Methods: Here, twenty-three C57BL/6 mice were divided into four groups and vaccinated with: G1: none (control); G2: GM-CSF DNA plasmid DNA vaccine; G3: the combination of EF-G+ENO+PGK; and G4: the combinations of EF-G+ENO+PGK proteins plus GM-CSF plasmid DNA vaccine. After 44 days, spleen cells were collected for immunophenotyping and lymphocyte proliferation evaluation by flow cytometry upon S. aureus stimulus. Results: Immunization with the three S. aureus recombinant proteins alone resulted in a higher percentage of IL-17A+ cells among CD8+ T central memory cells, as well as the highest intensity of IL-17A production by overall lymphocytes indicating that the contribution of the combined lymphocyte populations is crucial to sustaining a type 3 cell immunity environment. Conclusion: The immunization with three S. aureus-cure-associated recombinant proteins triggered type 3 immunity, which is a highly interesting path to pursue an effective bovine S. aureus mastitis vaccine

Census of Cnidaria (Medusozoa) and Ctenophora from South American marine waters

We have compiled available records in the literature for medusozoan cnidarians and ctenophores of South America. New records of species are also included. Each entry (i.e., identified species or still as yet not determined species referred to as "sp." in the literature) includes a synonymy list for South America, taxonomical remarks, notes on habit, and information on geographical occurrence. We have listed 800 unique determined species, in 958 morphotype entries: 5 cubozoans, 905 hydrozoans, 25 scyphozoans, 3 staurozoans, and 20 ctenophores. Concerning nomenclatural and taxonomical decisions, two authors of this census (Miranda, T.P. & Marques, A.C.) propose Podocoryna quitus as a nomen novum for the junior homonym Hydractinia reticulata (Fraser, 1938a); Euphysa monotentaculata Zamponi, 1983b as a new junior synonym of Euphysa aurata Forbes, 1848; and Plumularia spiralis Milstein, 1976 as a new junior synonym of Plumularia setacea(Linnaeus, 1758). Finally, we also reassign Plumularia oligopyxis Kirchenpauer, 1876 as Kirchenpaueria oligopyxis(Kirchenpauer, 1876) and Sertularella margaritacea Allman, 1885 as Symplectoscyphus margaritaceus (Allman, 1885).Fil: Oliveira, Otto M. P.. Universidade Federal de Santa Catarina; BrasilFil: Miranda, Thaís P.. Universidade de Sao Paulo; BrasilFil: Araujo, Enilma M.. Faculdade Pitágoras de Feira de Santana; BrasilFil: Ayón, Patricia. Instituto Mar del Peru; PerúFil: Cedeño Posso, Cristina M.. Instituto de Investigaciones Marinas y Costeras; ColombiaFil: Cepeda Mercado, Amancay A.. ProCAT Colombia/Internacional; ColombiaFil: Córdova, Pablo. Pontificia Universidad Católica de Chile; ChileFil: Cunha, Amanda F.. Universidade de Sao Paulo; BrasilFil: Genzano, Gabriel Nestor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Haddad, Maria Angélica. Universidade Federal do Paraná; BrasilFil: Mianzan, Hermes Walter. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Subsede Instituto Nacional de Investigación y Desarrollo Pesquero; ArgentinaFil: Migotto, Alvaro E.. Universidade de Sao Paulo; BrasilFil: Miranda, Lucília S.. Universidade de Sao Paulo; BrasilFil: Morandini, André C.. Universidade de Sao Paulo; BrasilFil: Nagata, Renato M.. Universidade de Sao Paulo; BrasilFil: Nascimento, Karine B.. Universidade de Sao Paulo; BrasilFil: Nogueira, Miodeli. Universidade Federal da Paraíba; BrasilFil: Palma, Sergio. Pontificia Universidad Católica de Chile; ChileFil: Quiñones, Javier. Instituto Mar del Perú; PerúFil: Rodriguez, Carolina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Scarabino, Fabrizio. Instituto Nacional de Investigaciones y Desarrollo Pesquero; ArgentinaFil: Schiariti, Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Subsede Instituto Nacional de Investigación y Desarrollo Pesquero; ArgentinaFil: Stampar, Sergio. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Tronolone, Valquíria B.. Universidade de Sao Paulo; BrasilFil: Marques, Antonio C.. Universidade de Sao Paulo; Brasi