AMBIENTE: UMA PALAVRA, MÚLTIPLAS DEFINIÇÕES

Os diferentes usos de ambiente e suas múltiplas correlações com outros termos, a exemplo de meio ambiente, dificultam a elaboração de uma clara e objetiva definição. Assim, este trabalho tem como objetivo apresentar, de maneira exploratória, as definições expostas nos principais dicionários de uso recorrente da língua portuguesa no Brasil, bem como nos sítios on-line dos principais programas de pós-graduação da área de Ciências Ambientais no Brasil, e, por fim, nas obras Epistemologia ambiental, de Enrique Leff, e Ambiente e território, de Marcelo Lopes de Souza, autores que se propõem a realizar uma reflexão crítica sobre os marcos conceituais. Com uso de técnicas quantitativas (nuvem de palavras) e técnicas qualitativas (quadro analítico), seguidas da sistematização das acepções nas obras citadas. Nota-se que os sentidos de ambiente são variados, tanto no cotidiano como nas áreas de conhecimento científico, o que resulta em uma vasta polissemia. Na reflexão dos autores elencados, há apontamentos originais que assinalam para uma forma de estruturar e organizar o pensamento, para se criar um ramo de conhecimento, um campo de saber interdisciplinar através do diálogo entre ciências da natureza e da sociedade, entre diferentes saberes e práticas

Lower insulin-dose adjusted A1c (IDAA1c) is associated with less complications in Individuals with Type 1 Diabetes treated with hematopoetic stem-cell transplantation and conventional therapy

Objective: To evaluate the association between insulin-dose adjusted A1C (IDAA1c) and microvascular complications (MC) and hypoglycemia in a representative Brazilian population of Type 1 diabetes mellitus (T1DM) patients. Research Design and Methods: This was a cross-sectional study based on a previous study, “Microvascular Complications in Type 1 Diabetes: a comparative analysis of patients treated with autologous nonmyeloablative hematopoietic stem-cell transplantation (AHST) and conventional medical therapy (CT)”. The 168 patients in that study (144 from CT plus 24 from AHST) were re-subdivided into two groups, according to their IDAA1c values (30 patients had IDAA1c ≤ 9; 138 had IDAA1c > 9). Then, the prevalence of MC (diabetic renal disease, neuropathy, and retinopathy), hypoglycemia (blood glucose <60 mg/dL), and severe hypoglycemic (episode of hypoglycemia that required the assistance of another person to treat) events were compared between the groups. The groups were well-matched on these factors: duration of disease, sex, and age at the time of diagnosis of T1DM. Results: After an average of 8 years after diagnosis, only 6.6% (2/30) of the patients from IDAA1c ≤ 9 group developed any MC, whereas 21.0% (29/138) from the IDAA1c > 9 group had at least one complication (p = 0.044). Regarding hypoglycemic events, the proportion of individuals who reported at least 1 episode of hypoglycemia in the last month was 43.3 and 64.7% from the IDAA1c ≤ 9 and IDAA1c > 9 groups, respectively (p = 0.030). Regarding severe hypoglycemia, the proportion of patients presenting at least one episode in the last month and the rate of episode/patient/month were similar between groups (6.7 vs. 13.2%; p = 0.535; and 0.1/patient/month vs. 0.25/patient/month; p = 0.321). Conclusion: In a representative Brazilian population of T1DM patients, those with IDAA1c ≤ 9 presented a lower frequency of MC, as well as fewer episodes of hypoglycemia, in the month prior to the analysis.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 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

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe

Educomunicação e suas áreas de intervenção: Novos paradigmas para o diálogo intercultural

oai:omp.abpeducom.org.br:publicationFormat/1O material aqui divulgado representa, em essência, a contribuição do VII Encontro Brasileiro de Educomunicação ao V Global MIL Week, da UNESCO, ocorrido na ECA/USP, entre 3&nbsp;e 5 de novembro de 2016. Estamos diante de um conjunto de 104 papers executivos, com uma média de entre 7 e 10 páginas, cada um. Com este rico e abundante material, chegamos ao sétimo e-book publicado pela ABPEducom, em seus seis primeiros anos de existência. A especificidade desta obra é a de trazer as “Áreas de Intervenção” do campo da Educomunicação, colocando-as a serviço de uma meta essencial ao agir educomunicativo: o diálogo intercultural, trabalhado na linha do tema geral do evento internacional: Media and Information Literacy: New Paradigms for Intercultural Dialogue

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