Cluster headache and intracranial aneurysm

In the present study we describe the cases of two patients with cluster-like headache related to intracranial carotid artery aneurysm. One of these patients responded to verapamil prescription with headache resolution. In both cases the surgical clipping of the aneurysm resolved the cluster pain. These findings strongly suggest a pathophysiological link between the two conditions. The authors discuss the potential pathophysiological mechanisms underlying cluster-like headache due to intracranial carotid artery aneurysm

Structural and optical properties of Er implanted AlN thin films: green and infrared photoluminescence at room temperature

In this work erbium ions were implanted into AlN films grown on sapphire with fluence range: (0.5-2) × 1015 at/cm-2, ion energy range: 150-350 keV and tilt angle: 0°, 10°, 20°, 30°. The optical and structural properties of the films are studied by means of photoluminescence and Raman spectroscopy in combination with Rutherford backscattering/channeling (RBS/C) measurements. The photoluminescence spectra of the Er3+ were recorded in the visible and infrared region between 9 and 300 K after thermal annealing treatments of the samples. The emission spectrum of the AlN:Er films consists of two series of green lines centered at 538 and 558 nm with typical Er3+ emission in the infrared at 1.54 μm. The green lines have been identified as Er3+ transitions from the 2H11/2 and 4S3/2 levels to the 4I15/2 ground state. Different erbium centers in the matrix are suggested by the change of infrared photoluminescence relative intensity of some of the emission lines when different excitation wavelengths are used. The relative abundances of these centers can be varied by using different implantation parameters. The Raman and RBS/C measurements show good crystalline quality for all the studied films.PTDC/CTM/100756/2008SFRH/BD/45774/2008Portuguese Agency GRICESBrazilian Agency CAPES the Grant 172/0

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Composição florística de uma floresta ripária na Reserva Estadual de Porto Ferreira, SP Floristic composition of a riparian forest area in Porto Ferreira State Reserve, State of São Paulo, S.E. Brasil

Aplicou-se o método de quadrantes (63 pontos) na Reserva Estadual de Porto Ferreira (21º49'S e 47º25'W), numa área (l,08ha) à margem direita do rio Moji Guaçu, amostrando dois indivíduos lenhosos em cada quadrante: um com fuste mínimo de 130cm e DAP < 10 cm; outro com DAP > 10cm. Os resultados obtidos foram comparados com os publicados por outros autores para uma área de mata riparia na Estação Ecológica de Moji Guaçu (Mata da Figueira), cerca de 100 km a montante daquele rio. Em Porto Ferreira, encontraram-se 107 espécies, sendo 80 exclusivas. Das 59 espécies listadas por outros autores para a Mata da Figueira, 31 foram exclusivas. As duas áreas tiveram 27 espécies comuns, com uma similaridade de Sørensen de 48,6%, considerada baixa. A grande heterogeneidade ambiental das várzeas e os diferentes graus de perturbação antrópica poderiam contribuir para essa variação florística. Os maiores números de espécies ocorreram em Leguminosae (20), Myrtaceae (17), Rutaceae (9), Euphorbiaceae (7) e Lauraceae, Meliaceae, Moraceae e Rubiaceae (6 espécies cada). Ao nível de família, parece haver poucas diferenças'com as florestas paulistas não inundáveis, mas as espécies mostram diferentes graus de preferência pelo habitat. As duas áreas apresentaram uma mistura de espécies típicas com outras de florestas nâo inundáveis. Estas ocorreriam na várzea em decorrência de, principalmente: a) adaptações do sistema radicular a períodos relativamente curtos de inundação; b) menor tempo de inundação nos pontos mais elevados do microrrelevo da várzea; c) maior aeração provocada pela água corrente.<br>The point-centred quarter method (63 points) was applied in Porto Ferreira State Reserve (21º49'S and 47º25'W) in an area (1.08ha) on the right margin of Moji Guaçu river, including two woody individuals per quarter - one with DBH < 10cm and at least 130cm high, the other with DBH > 10cm. The results obtained were compared with those published by other authors for a riparian forest (Mata da Figueira) at Moji Guaçu Ecological Station (about 100 km upstream on the same river). At Porto Ferreira 107 species were found, of which 80 were exclusive, compared with the Mata da Figueira where of the 59 species listed, 31 were exclusive. The two area shared 27 common species, thus accounting for a low Sørensen similarity of 48.6%. The great environmental heterogeneity of the floodplains, as well as the degree of anthropic disturbance, could account for this floristic variation. The greatest numbers of species were shown by Leguminosae (20), Myrtaceae (17), Rutaceae (9), Euphorbiaceae (7), and Lauraceae, Meliaceae, Moraceae and Rubiaceae (6 species each). There appears to be little difference at the family level among the periodically flooded and non-flooded forests of the State of São Paulo, but the species show different degreees of preference for habitat. The floristic composition of the two areas presented a mixture of typical species with others of non-flooded forests. The latter would occur on the floodplain probably by a) adaptation of the root system to relatively short flooding periods; b) shorter periods of flooding on the higher points of the microrelief of the floodplain, and c) greater aeration due to running water