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

The investigation of the presence of toxic granulation for septicemia hematologic diagnostic

This work aims at investigating the association of the presence of toxic granulation with positive blood cultures, age of patients, conditions of hospitalization and types of bacterial agents. Blind prospective and retrospective, analyses were carried out for the presence of toxic granulations-in blood samples of 300 patients of the both genders hospitalized in the City of Belém, Pará, Brazil. Request blood tests over a two year period were evaluated. The blood tests and cultures were performes using automated methods. All the data were statistically compared using the Qui-square test (clump method). The results show statistical associations between: (1) the presence of toxic granulations and positive blood cultures; (2) lower ages of patients (the newborn) and positive blood cultures; (3) hospitalization in the ICU and positive blood cultures and (4) toxic granulations-and the observation of leucocytosis and right-left shunts in patients hospitalized in the ICU with positive blood cultures. The commonest bacterial agents identified were klebsiella oxytoca (22%), Acinetobacter calcoaceticus (20%), Escherichia coli (18%), Enterobacter cloacae (14%), and Pseudomonas aeruginosa (8%).Este trabalho visou investigar a associação da coexistência da presença de granulações tóxicas com resultados de hemocultura positivas, idade dos pacientes, condições de internamento e tipos de agentes bacterianos. Foi realizada análise retrospectiva e prospectiva, cega, para a presença de granulações tóxicas em amostras sangüíneas de trezentos pacientes, de ambos os sexos, internados em hospitais da Cidade de Belém – Pará, com solicitação de hemocultura, num período de dois anos. Com os hemogramas e as hemoculturas realizadas por métodos de automação, e todos os dados submetidos à metodologia de comparação estatística pelo Qui-quadrado (método de clump). Nossos resultados mostraram a existência de associação estatística entre: (1) a presença de granulações tóxicas e os resultados de hemoculturas positivas; (2) a menor idade dos pacientes (neonatos) associadas a hemocultura positiva; (3) a condição de internamento em UTI com hemocultura positiva; e (4) a presença de granulações tóxicas e a observação de leucocitose e desvio à esquerda, em pacientes internados em UTI, com hemoculturas positivas. E que os cinco principais agentes bacterianos identificados nas hemoculturas deste estudo foram Klebsiella oxytoca (22%), Acinetobacter calcoaceticus (20%), Escherichia coli (18%), Enterobacter cloacae (14%), e Pseudomonas aeruginosa (8%)