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

Geo-spatial data integration for mapping landscape units in the Tapajós region

The stratification of the ecosystem in homogeneous regions is crucial for determining the spatial variation of environment variables in studies related to the carbon dynamics in the Amazonia. Based on the hypothesis that landscape heterogeneity is determined by the interaction of the different types of vegetation, relief and land use, the principal aim of this research was to present a methodological routine to generate a Landscape Unit (LU) map for the Tapajos region. The study area is localized between the latitudes 02o 24’ 2’’ S and 04o 01’ 1’’ S, and longitudes 55o 30’ 2’’ W e 54o 29’ 5’’ W, in the Para State. Boolean logic operations were applied for the integration of the thematic maps containing the information about landscape attributes. The LU map showed that despite primary forests is the dominant vegetation type in the region, around 28% of the study area suffered human intervention. The proposed routine was efficient in characterizing the landscape heterogeneity. The advantages of this method are the preservation of more representative vegetation types and the reduction of the number of sample units. This mapping is important for helping regional scale researches using from a high to a moderate spatial resolution approach (from 30 to 500 meters)

Validação do mapeamento de uma área de floresta tropical com o uso imagens de videografia aérea e dados de levantamento de campo Validation of tropical forest area mapping using aerial videography images and data from field work survey

Este trabalho apresenta o mapeamento da cobertura vegetal da região da Floresta Nacional do Tapajós (FNT) no Pará, realizado por imagens multitemporais do satélite Landsat. Para a validação do mapeamento, foram utilizadas imagens de videografia aérea e dados de levantamento de campo. Através da análise da matriz de confusão, foram observados uma exatidão global de classificação de 84,5% e um índice kappa de 80,9%. O uso dos mosaicos de videografia aérea e dos pontos de levantamento de campo, dentro de um sistema de informação geográfica, permitiu caracterizar e avaliar a qualidade do mapeamento da região da FNT.<br>A vegetation cover mapping of Tapajós National Forest (FNT) in the State of Pará, by Landsat multitemporal images is presented. For mapping validation purposes, aerial videography images and field work survey were used. The confusion matrix analysis gave a 84.5% global classification accuracy and a kappa coefficient of 80.9%. The use of aerial videography mosaics and plots of field work survey allowed to characterize and to evaluate the quality of FNT region mapping

The Brazilian Registry of Adult Patient Undergoing Cardiovascular Surgery, the BYPASS Project: Results of the First 1,722 Patients

Abstract Objective: To report the early results of the BYPASS project - the Brazilian registrY of adult Patient undergoing cArdiovaScular Surgery - a national, observational, prospective, and longitudinal follow-up registry, aiming to chart a profile of patients undergoing cardiovascular surgery in Brazil, assessing the data harvested from the initial 1,722 patients. Methods: Data collection involved institutions throughout the whole country, comprising 17 centers in 4 regions: Southeast (8), Northeast (5), South (3), and Center-West (1). The study population consists of patients over 18 years of age, and the types of operations recorded were: coronary artery bypass graft (CABG), mitral valve, aortic valve (either conventional or transcatheter), surgical correction of atrial fibrillation, cardiac transplantation, mechanical circulatory support and congenital heart diseases in adults. Results: 83.1% of patients came from the public health system (SUS), 9.6% from the supplemental (private insurance) healthcare systems; and 7.3% from private (out-of -pocket) clinic. Male patients comprised 66%, 30% were diabetics, 46% had dyslipidemia, 28% previously sustained a myocardial infarction, and 9.4% underwent prior cardiovascular surgery. Patients underwent coronary artery bypass surgery were 54.1% and 31.5% to valve surgery, either isolated or combined. The overall postoperative mortality up to the 7th postoperative day was 4%; for CABG was 2.6%, and for valve operations, 4.4%. Conclusion: This first report outlines the consecution of the Brazilian surgical cardiac database, intended to serve primarily as a tool for providing information for clinical improvement and patient safety and constitute a basis for production of research protocols

Paediatric COVID-19 mortality: a database analysis of the impact of health resource disparity

Background The impact of the COVID-19 pandemic on paediatric populations varied between high-income countries (HICs) versus low-income to middle-income countries (LMICs). We sought to investigate differences in paediatric clinical outcomes and identify factors contributing to disparity between countries.Methods The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) COVID-19 database was queried to include children under 19 years of age admitted to hospital from January 2020 to April 2021 with suspected or confirmed COVID-19 diagnosis. Univariate and multivariable analysis of contributing factors for mortality were assessed by country group (HICs vs LMICs) as defined by the World Bank criteria.Results A total of 12 860 children (3819 from 21 HICs and 9041 from 15 LMICs) participated in this study. Of these, 8961 were laboratory-confirmed and 3899 suspected COVID-19 cases. About 52% of LMICs children were black, and more than 40% were infants and adolescent. Overall in-hospital mortality rate (95% CI) was 3.3% [=(3.0% to 3.6%), higher in LMICs than HICs (4.0% (3.6% to 4.4%) and 1.7% (1.3% to 2.1%), respectively). There were significant differences between country income groups in intervention profile, with higher use of antibiotics, antivirals, corticosteroids, prone positioning, high flow nasal cannula, non-invasive and invasive mechanical ventilation in HICs. Out of the 439 mechanically ventilated children, mortality occurred in 106 (24.1%) subjects, which was higher in LMICs than HICs (89 (43.6%) vs 17 (7.2%) respectively). Pre-existing infectious comorbidities (tuberculosis and HIV) and some complications (bacterial pneumonia, acute respiratory distress syndrome and myocarditis) were significantly higher in LMICs compared with HICs. On multivariable analysis, LMIC as country income group was associated with increased risk of mortality (adjusted HR 4.73 (3.16 to 7.10)).Conclusion Mortality and morbidities were higher in LMICs than HICs, and it may be attributable to differences in patient demographics, complications and access to supportive and treatment modalities