O cultivo do arroz e a resposta do agrossistema às alterações ambientais de temperatura e dióxido de carbono

A cultivar de arroz Ariete apresentou uma eficiência máxima de uso do N de 64% e uma produção de 8,5 t ha-1, após aplicação de 120 kg N ha-1, metade em fundo e metade ao afilhamento. Os fatores de emissão de GEEs medidos no campo foram de 136 kg CH4 ha-1 e 1,5% para o N2O. As emissões de COVs (especialmente na forma de terpenos) e NH3 foram reduzidas, mas atingiram 8 kg N-NH3 ha-1 dia-1 após a adubação de cobertura.FCT - projeto PTDC/AGR-AAM/102529/2008

Tau-dependent suppression of adult neurogenesis in the stressed hippocampus

uncorrected proofStress, a well-known sculptor of brain plasticity, is shown to suppress hippocampal neurogenesis in the adult brain; yet, the underlying cellular mechanisms are poorly investigated. Previous studies have shown that chronic stress triggers hyperphosphorylation and accumulation of the cytoskeletal protein Tau, a process that may impair the cytoskeleton-regulating role (s) of this protein with impact on neuronal function. Here, we analyzed the role of Tau on stress-driven suppression of neurogenesis in the adult dentate gyrus (DG) using animals lacking Tau (Tau-knockout; Tau-KO) and wild-type (WT) littermates. Unlike WTs, Tau-KO animals exposed to chronic stress did not exhibit reduction in DG proliferating cells, neuroblasts and newborn neurons; however, newborn astrocytes were similarly decreased in both Tau-KO and WT mice. In addition, chronic stress reduced phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR)/glycogen synthase kinase-3 beta (GSK3 beta)/beta-catenin signaling, known to regulate cell survival and proliferation, in the DG of WT, but not Tau-KO, animals. These data establish Tau as a critical regulator of the cellular cascades underlying stress deficits on hippocampal neurogenesis in the adult brain.Portuguese Foundation for Science and Technology (FCT) Investigator grants (IF/01799/2013, IF/00883/2013, IF/01079/2014, respectively). This work was funded by FCT research grants 'PTDC/SAU-NMC/113934/2009' (IS), the Portuguese North Regional Operational Program (ON.2) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER), the Project Estratégico co-funded by FCT (PEst-C/SAU/LA0026/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298) as well as the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER)info: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 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

Dream reflecting cultural contexts: comparing Brazilian and German diary dreams and most recent dreams

The continuity hypothesis states dat waking life experiences, e.g., personal concerns and cultural environment, are reflected in dreams. The present study’s goal was to compare written dream reports from Brazilian and German samples. Overall, 97 Brazilian participants were included; two German samples from previous studies (N=89 and N=4197) were selected for comparison. The Brazilian participants reported 328 and the German participants 348 diary dreams. In addition, 86 Brazilian too, 2893 German most recent dreams were compared. For both types of collected dreams, we found more bizarreness in German reports and more depression-related topics in Brazilian reports. Germans reported more aggression in diary dreams and both more problems and more extreme emotions in most recent dreams. Brazilians reported more work- and leisure-related topics in diary dreams. Several similarities in dream content, e.g., no differences in verbal and physical interaction, number of male and female characters, and death-related themes hint at universal characteristics of dreaming across cultures. We consider the clear group differences in dream content to reflect basic patterns of Brazilian and German cultures, which are exemplified through some core topics of the respective cultural sets. Concerning Brazil, the themes of miscegenation, emotional culture, and “saudade” (“longing”) are discussed. For German culture, the area of conflict between long-standing high-performance in science and arts and the contrasting twentieth-century history are addressed. Future research should study the relation between dream content and culture in representative samples; to this purpose dream diaries, sampling daily average dreams, seem preferable to most recent dreams.info:eu-repo/semantics/publishedVersio