11 research outputs found
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
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
Enterotoxigenic and nontoxigenic Bacteroides fragilis strains isolated in Brazil
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Previous issue date: 2008Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Mèdica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Mèdica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Mèdica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instittuto Oswaldo Cruz. Laboratório de Zoonoses Bacterianas. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal Fluminense. Faculdade de Farmácia. Departamento de Tecnologia Farmacêutica. Niterói, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Zoonoses Bacterianas. Rio de Janeiro, RJ. Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Zoonoses Bacterianas. Rio de Janeiro, RJ. Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.The presence of enterotoxigenic Bacteroides fragilis and nontoxigenic B. fragilis (NTBF) among 109 strains
isolated from 1980-2008 in Brazil were investigated by PCR. One strain, representing 0.9% of the total analyzed
strains, harbored the bft gene which was identified as bft-1 isoform based on PCR-RFLP and sequencing. Fortynine
strains (44.9%) exhibited the NTBF pattern III which possesses the flanking region required for pathogenicity
island acquisition in which the bft gene is codified. These data reinforce the potential of B. fragilis as an emerging
enteropathogen in our country
Enterotoxigenic and nontoxigenic Bacteroides fragilis strains isolated in Brazil
The presence of enterotoxigenic Bacteroides fragilis and nontoxigenic
B. fragilis (NTBF) among 109 strains isolated from 1980-2008 in Brazil
were investigated by PCR. One strain, representing 0.9% of the total
analyzed strains, harbored the bft gene which was identified as bft-1
isoform based on PCR-RFLP and sequencing. Forty-nine strains (44.9%)
exhibited the NTBF pattern III which possesses the flanking region
required for pathogenicity island acquisition in which the bftgene is
codified. These data reinforce the potential of B. fragilis as an
emerging enteropathogen in our country