ALFABETIZAÇÃO EDUCAÇÃO DE JOVENS E ADULTOS: O PROCESSO DE CONSTRUÇÃO DA ESCRITA

ALFABETIZAÇÃO EDUCAÇÃO DE JOVENS E ADULTOS: O PROCESSO DE CONSTRUÇÃO DA ESCRIT

ALFABETIZAÇÃO EDUCAÇÃO DE JOVENS E ADULTOS: O PROCESSO DE CONSTRUÇÃO DA ESCRITA

ALFABETIZAÇÃO EDUCAÇÃO DE JOVENS E ADULTOS: O PROCESSO DE CONSTRUÇÃO DA ESCRIT

Avaliação da importância de modelos no ensino de biologia através da aplicação de um modelo demonstrativo da junção intercelular desmossomo

The present work involved the development of a representative model of the desmosome junction and its application to 172 students divided into beginners (1o semester of Nutrition and Biological Sciences) and veterans (2o of Nutrition and 4o of Biological Sciences) to test its effectiveness on the teaching-learning process. It was found that 78% of beginners and 82% of veterans were female; 98% had between 17 and 30 years-old; the majority (61% and 50%, beginners and veterans, respectively) assumed it was the first occasion they had contact with a model although at least 90% of them recognized the main represented structure (the desmosome); the majority of both groups said that the model allowed the recognition of junctional structures; 97% of beginners and 99% of the veterans declared that their knowledge about intercellular junctions and desmosome enhanced and 72% of beginners affirmed that their teachers possess models for practical classes compared to 33% in veterans. The attribution of scores exceeding 7 to the model revealed that its use in practical lessons is productive.O presente trabalho consistiu na elaboração de um modelo demonstrativo da junção desmossomo e na sua aplicação a 172 estudantes divididos em iniciantes (1o semestres de Nutrição e Ciências Biológicas) e veteranos (2o de Nutrição e 4o de Ciências Biológicas) para testar sua eficácia para o processo de ensino-aprendizagem. Verificou-se que 78% dos iniciantes e 82% dos veteranos eram do sexo feminino; 98% tinham entre 17 e 30 anos de idade; a maioria (61% e 50%, iniciantes e veteranos, respectivamente) afirmaram que era a primeira vez que tiveram contato com um modelo embora pelo menos 90% deles reconheceram a principal estrutura representada (o desmossomo); a maioria de ambos os grupos afirmaram que o modelo permitiu o reconhecimento das estruturas juncionais; 97% dos iniciantes e 99% dos veteranos afirmaram que seu conhecimento sobre junções intercelulares e desmossomo melhorou e 72% dos iniciantes disseram que seus professores dispõem de modelos para as aulas práticas contra apenas 33% dos veteranos. A atribuição de notas acima de 7 ao modelo revelou que sua utilização em aulas práticas é profícua.Palavras-chave: instrumentos, junções, aulas práticas

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

Statement of Second Brazilian Congress of Mechanical Ventilarion : part I

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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

Studies of the mass composition of cosmic rays and proton-proton interaction cross-sections at ultra-high energies with the Pierre Auger Observatory

In this work, we present an estimate of the cosmic-ray mass composition from the distributions of the depth of the shower maximum (Xmax) measured by the fluorescence detector of the Pierre Auger Observatory. We discuss the sensitivity of the mass composition measurements to the uncertainties in the properties of the hadronic interactions, particularly in the predictions of the particle interaction cross-sections. For this purpose, we adjust the fractions of cosmic-ray mass groups to fit the data with Xmax distributions from air shower simulations. We modify the proton-proton cross-sections at ultra-high energies, and the corresponding air shower simulations with rescaled nucleus-air cross-sections are obtained via Glauber theory. We compare the energy-dependent composition of ultra-high-energy cosmic rays obtained for the different extrapolations of the proton-proton cross-sections from low-energy accelerator data

Study of downward Terrestrial Gamma-ray Flashes with the surface detector of the Pierre Auger Observatory

The surface detector (SD) of the Pierre Auger Observatory, consisting of 1660 water-Cherenkov detectors (WCDs), covers 3000 km2 in the Argentinian pampa. Thanks to the high efficiency of WCDs in detecting gamma rays, it represents a unique instrument for studying downward Terrestrial Gamma-ray Flashes (TGFs) over a large area. Peculiar events, likely related to downward TGFs, were detected at the Auger Observatory. Their experimental signature and time evolution are very different from those of a shower produced by an ultrahigh-energy cosmic ray. They happen in coincidence with low thunderclouds and lightning, and their large deposited energy at the ground is compatible with that of a standard downward TGF with the source a few kilometers above the ground. A new trigger algorithm to increase the TGF-like event statistics was installed in the whole array. The study of the performance of the new trigger system during the lightning season is ongoing and will provide a handle to develop improved algorithms to implement in the Auger upgraded electronic boards. The available data sample, even if small, can give important clues about the TGF production models, in particular, the shape of WCD signals. Moreover, the SD allows us to observe more than one point in the TGF beam, providing information on the emission angle