Educação, Acesso à Informação e Participação Popular: Uma Análise das Medidas do Estado do Pará Acerca da Tentativa de Adoção de Escolas Charter

This article aims to discuss the popular participation within the State of Pará measures, which tried to use school model charter for improvement of public education. For this, it is made a brief explanation about popular participation and its relation to the right to information. Then, it is explained what is a charter school and the different forms of use of private apparatus by the State. We approach the news about the governement’s measures and the lack of clear and accessible information on official vehicles. Finally, we present the conclusions.Este artigo visa debater acerca da participação popular nas medidas do Estado do Pará, o qual tentou utilizar o modelo de escola charter para melhorias da educação pública. Para isso, é feita uma breve explanação acerca da participação popular e sua relação com o direito à informação. Em seguida, é explicado no que consiste uma escola charter e as diferentes formas de uso do aparato privado. Abordamos as notícias sobre as medidas do Governo estadual e a falta de informações claras e acessíveis nos veículos oficiais. Por fim, apresentam-se as conclusões

Detección de enterobacterias multirresistentes aisladas en aguas de los ríos que desembocan en la bahía de Guanabara y en muestras de hospitales de Río de Janeiro, Brasil

Introduction: The use of antibiotics in humans, animal husbandry and veterinary activities induces selective pressure leading to the colonization and infection by resistant strains.Objective: We evaluated water samples collected from rivers of the Guanabara Bay, which have suffered minor and major environmental degradation, and clinical samples of hospital origin to detect evidence of the presence of resistance genes to aminoglycosides, beta-lactam antibiotics and fluoroquinolones in strains of Klebsiella pneumoniae subsp. pneumoniae, K. pneumoniae subsp. ozaenae and Escherichia coli.Materials and methods: For isolation of the water strains we employed culture media containing 32 μg/ml cephalotin and 8 μg/ml gentamicin. The strains from clinical materials were selected using culture media containing 8 μg/ml gentamicin. The strains were identified and subjected to antimicrobial susceptibility testing (AST), plasmid DNA extraction and polymerase chain reaction (PCR) to detect genes encoding enzymes modifying aminoglycosides (EMA), extended-spectrum beta-lactamases (ESBL) and plasmid mechanisms of quinolone resistance (PMQR).Results: The AST of the isolates recovered from water samples showed multidrugresistance profiles similar to those found in isolates recovered from clinical materials. All isolates from water samples and 90% of the isolates from clinical samples showed at least one plasmid band. In the PCR assays, 7.4% of the isolates recovered from water samples and 20% of those from clinical materials showed amplification products for the three antimicrobial classes.Conclusion: We believe that the detection of microorganisms presenting genetic elements in environments such as water is necessary for the prevention and control of their dissemination with potential to infect humans and other animals in eventual contact with these environments.Introducción. El uso de antibióticos en seres humanos, en la industria pecuaria y en las actividades veterinarias induce una presión selectiva que resulta en la colonización e infección con cepas resistentes.Objetivo. Determinar la presencia de genes de resistencia a aminoglucósidos, betalactámicos y fluoroquinolonas en cepas de Klebsiella pneumoniae subsp. pneumoniae, K. pneumoniae subsp. ozaenae y Escherichia coli, obtenidas de muestras de agua de los ríos que desembocan en la bahía de Guanabara y de muestras clínicas de hospitales de Río de Janeiro. Materiales y métodos. En la selección de las cepas resistentes obtenidas de las muestras de agua de los ríos, se emplearon medios de cultivo que contenían 32 μg/ml de cefalotina y 8 μg/ml de gentamicina. En el caso de las muestras de especímenes clínicos, se usaron medios de cultivo que contenían 8 μg/ml de gentamicina. Las cepas se identificaron y se sometieron a pruebas de sensibilidad antimicrobiana, extracción de ADN plasmídico y pruebas de reacción en cadena de la polimerasa (PCR) para detectar los genes que codifican aquellas enzimas que modifican los aminoglucósidos, las betalactamasas de espectro extendido (BLEE) y los mecanismos de resistencia a las quinolonas mediados por plásmidos.Resultados. Se encontraron perfiles de resistencia a los antimicrobianos similares en los dos grupos. En todas las bacterias obtenidas de las muestras de agua y en 90 % de las muestras clínicas, se evidenciaron bandas de plásmidos asociados con la transferencia de genes de resistencia. En las pruebas de PCR, se obtuvieron productos de amplificación de los genes de resistencia para las tres clases de antimicrobianos analizados, en el 7,4 % de las bacterias recuperadas de las muestras de agua y en el 20 % de aquellas recuperadas de las muestras clínicas.Conclusión. La detección de microorganismos con elementos genéticos que confieren resistencia a los antibióticos en ambientes como el agua, es una estrategia necesaria para prevenir y controlar la diseminación de estos agentes patógenos con potencial para infectar a humanos y a otros animales en dichos ambientes

High anti-SARS-CoV-2 antibody seroconversion rates before the second wave in Manaus, Brazil, and the protective effect of social behaviour measures: results from the prospective DETECTCoV-19 cohort

Background: The city of Manaus, Brazil, has seen two collapses of the health system due to the COVID-19 pandemic. We report anti-SARS-CoV-2 nucleocapsid IgG antibody seroconversion rates and associated risk factors in Manaus residents before the second wave of the epidemic in Brazil. Methods: A convenience sample of adult (aged ≥18 years) residents of Manaus was recruited through online and university website advertising into the DETECTCoV-19 study cohort. The current analysis of seroconversion included a subgroup of DETECTCoV-19 participants who had at least two serum sample collections separated by at least 4 weeks between Aug 19 and Oct 2, 2020 (visit 1), and Oct 19 and Nov 27, 2020 (visit 2). Those who reported (or had no data on) having a COVID-19 diagnosis before visit 1, and who were positive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at visit 1 were excluded. Using an in-house ELISA, the reactivity index (RI; calculated as the optical density ratio of the sample to the negative control) for serum anti-SARS-CoV-2 nucleocapsid IgG antibodies was measured at both visits. We calculated the incidence of seroconversion (defined as RI values ≤1·5 at visit 1 and ≥1·5 at visit 2, and a ratio >2 between the visit 2 and visit 1 RI values) during the study period, as well as incidence rate ratios (IRRs) through cluster-corrected and adjusted Poisson regression models to analyse associations between seroconversion and variables related to sociodemographic characteristics, health access, comorbidities, COVID-19 exposure, protective behaviours, and symptoms. Findings: 2496 DETECTCoV-19 cohort participants returned for a follow-up visit between Oct 19 and Nov 27, 2020, of whom 204 reported having COVID-19 before the first visit and 24 had no data regarding previous disease status. 559 participants were seropositive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at baseline. Of the remaining 1709 participants who were seronegative at baseline, 71 did not meet the criteria for seroconversion and were excluded from the analyses. Among the remaining 1638 participants who were seronegative at baseline, 214 showed seroconversion at visit 2. The seroconversion incidence was 13·06% (95% CI 11·52–14·79) overall and 6·78% (5·61–8·10) for symptomatic seroconversion, over a median follow-up period of 57 days (IQR 54–61). 48·1% of seroconversion events were estimated to be asymptomatic. The sample had higher proportions of affluent and higher-educated people than those reported for the Manaus city population. In the fully adjusted and corrected model, risk factors for seroconversion before visit 2 were having a COVID-19 case in the household (IRR 1·49 [95% CI 1·21–1·83]), not wearing a mask during contact with a person with COVID-19 (1·25 [1·09–1·45]), relaxation of physical distancing (1·31 [1·05–1·64]), and having flu-like symptoms (1·79 [1·23–2·59]) or a COVID-19 diagnosis (3·57 [2·27–5·63]) between the first and second visits, whereas working remotely was associated with lower incidence (0·74 [0·56–0·97]). Interpretation: An intense infection transmission period preceded the second wave of COVID-19 in Manaus. Several modifiable behaviours increased the risk of seroconversion, including non-compliance with non-pharmaceutical interventions measures such as not wearing a mask during contact, relaxation of protective measures, and non-remote working. Increased testing in high-transmission areas is needed to provide timely information about ongoing transmission and aid appropriate implementation of transmission mitigation measures. Funding: Ministry of Education, Brazil; Fundação de Amparo à Pesquisa do Estado do Amazonas; Pan American Health Organization (PAHO)/WHO.World Health OrganizationRevisión por pare

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

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

Aim: Amazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. Location: Amazonia. Taxon: Angiosperms (Magnoliids; Monocots; Eudicots). Methods: Data for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran\u27s eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. Results: In the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R$^{2}$ = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R$^{2}$ = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main Conclusion: Numerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions

Geographic patterns of tree dispersal modes in Amazonia and their ecological correlates

Aim: To investigate the geographic patterns and ecological correlates in the geographic distribution of the most common tree dispersal modes in Amazonia (endozoochory, synzoochory, anemochory and hydrochory). We examined if the proportional abundance of these dispersal modes could be explained by the availability of dispersal agents (disperser-availability hypothesis) and/or the availability of resources for constructing zoochorous fruits (resource-availability hypothesis). Time period: Tree-inventory plots established between 1934 and 2019. Major taxa studied: Trees with a diameter at breast height (DBH) ≥ 9.55 cm. Location: Amazonia, here defined as the lowland rain forests of the Amazon River basin and the Guiana Shield. Methods: We assigned dispersal modes to a total of 5433 species and morphospecies within 1877 tree-inventory plots across terra-firme, seasonally flooded, and permanently flooded forests. We investigated geographic patterns in the proportional abundance of dispersal modes. We performed an abundance-weighted mean pairwise distance (MPD) test and fit generalized linear models (GLMs) to explain the geographic distribution of dispersal modes. Results: Anemochory was significantly, positively associated with mean annual wind speed, and hydrochory was significantly higher in flooded forests. Dispersal modes did not consistently show significant associations with the availability of resources for constructing zoochorous fruits. A lower dissimilarity in dispersal modes, resulting from a higher dominance of endozoochory, occurred in terra-firme forests (excluding podzols) compared to flooded forests. Main conclusions: The disperser-availability hypothesis was well supported for abiotic dispersal modes (anemochory and hydrochory). The availability of resources for constructing zoochorous fruits seems an unlikely explanation for the distribution of dispersal modes in Amazonia. The association between frugivores and the proportional abundance of zoochory requires further research, as tree recruitment not only depends on dispersal vectors but also on conditions that favour or limit seedling recruitment across forest types

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

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