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

Development of an experimental bench for tests of autonomous land vehicles

Neste trabalho é realizado o desenvolvimento de uma bancada experimental para avaliar esquemas de controle para robôs terrestres autônomos visando potenciais aplicações no controle de um carro que se movimente de forma autônoma no trânsito de uma cidade. Na primeira etapa deste projeto, especifica-se todo o hardware da bancada, a aquisição dos robôs, a montagem de toda a bancada, assim como o desenvolvimento e codificação do firmware de comunicação dos robôs, a modelagem matemática e o desenvolvimento do simulador dos robôs. Como segunda etapa destaca-se o desenvolvimento de um software que engloba diversas funcionalidades, como: os algoritmos para obtenção da posição e orientação de cada robô, baseados em visão computacional, os algoritmos de controle, algoritmos de aquisição e visualização de dados e a interface com o usuário. A estratégia de controle desenvolvida visa permitir que o veículo evite colisões em um cruzamento, por exemplo, que permaneça a uma distância segura do veículo a sua frente em uma velocidade de cruzeiro pré-estabelecida (cruise control) e que permita que o veículo realize ultrapassagens seguras. Os diversos sensores necessários em uma aplicação real são emulados pela informação de posição e orientação de cada veículo obtidas por meio de visão computacional. O esquema de controle de cada veículo utiliza apenas as informações do veículo mais próximo afim de emular uma situação real. Da modelagem completa de cada veículo, robôs terrestres tipo uniciclo, um modelo simples é obtido e utilizado para projeto do controle. O modelo completo constitui o simulador desenvolvido. Os resultados experimentais com dois robôs evidenciam a eficácia do esquema de controle utilizado

20140402150657_IPCAM

Real operation scene This scene was recorded during a real Irradiation operation, more specifically during its final tasks (removing the irradiated sample). This scene was an extra recording to the script and planned ones. - Scene: Involved a number of persons, as: two operators, two personnel belonging to the radiological protection service, and the "client" who asked for the irradiation. Video file labels: "20140402150657_IPCAM": recorded by the right camera.Involved a number of persons, as: two operators, two personnel belonging to the radiological protection service, and the "client" who asked for the irradiation

"20140327182905_IPCAM"

Scenes for Spectrography experiment Scenes were recorded following the tasks involved in spectrography experiments, which are carried out in front of "J9" output radiadion channel, the latter in open condition. These tasks may be executed by one or two persons. One person can do the tasks, but requiring him to crouch in front of "J9" to adjust the angular position the experimental appartus (a crystal to bend the neutron radiation to the spectograph), and then to get up to verify data in a computer aside; these movements are repeated until achieving the right operational conditions. Two people may aid one another in such a way one remais crouched while the other remains still in front of the computer. They may also interchange tasks so as to divide received doses. Up to now, there are available two scenes with one person and one scene with two persons. These scenes are described in the sequel: - Scene 3: Comprises the scene with two persons performing spectography experiment. Video file labels: "20140327182905_IPCAM": recorded by the right camera.Comprises the scene with two persons performing spectography experiment

"20140326154755_IPCAM"

General simulated scenes These scenes followed a pre-defined script (see the Thesis for details), with common movements corresponding to general experiments. People go to or stand still in front of "J9", and/or go to the side of Argonauta reactor and come back again. The first type of movement is common during Irradiation experiments, where a material sample is put within the "J9" channel; and also during neutrongraphy or gammagraphy experiments, where a sample is placed in front of "J9". Here, the detailed movements of putting samples on these places were not reproduced in details, but only the whole bodies' movements were simulated (as crouching or being still in front of "J9"). The second type of movement may occur when operators go to the side of Argonauta to verify some operational condition. - Scene 2: Comprises one of the scenes with two persons. Both of them use clothes of dark colors. Both persons go to the side of Argonauta reactor and then come back and go out. Video file labels: "20140326154755_IPCAM": recorded by the left camera.Comprises one of the scenes with two persons. Both of them use clothes of dark colors. Both persons go to the side of Argonauta reactor and then come back and go out

20140326154754_IPCAM

General simulated scenes These scenes followed a pre-defined script (see the Thesis for details), with common movements corresponding to general experiments. People go to or stand still in front of "J9", and/or go to the side of Argonauta reactor and come back again. The first type of movement is common during Irradiation experiments, where a material sample is put within the "J9" channel; and also during neutrongraphy or gammagraphy experiments, where a sample is placed in front of "J9". Here, the detailed movements of putting samples on these places were not reproduced in details, but only the whole bodies' movements were simulated (as crouching or being still in front of "J9"). The second type of movement may occur when operators go to the side of Argonauta to verify some operational condition. - Scene 2: Comprises one of the scenes with two persons. Both of them use clothes of dark colors. Both persons go to the side of Argonauta reactor and then come back and go out. Video file labels: "20140326154754_IPCAM": recorded by the right camera.Comprises one of the scenes with two persons. Both of them use clothes of dark colors. Both persons go to the side of Argonauta reactor and then come back and go out

Libidibia ferrea Mature Seeds Promote Antinociceptive Effect by Peripheral and Central Pathway: Possible Involvement of Opioid and Cholinergic Receptors

Libidibia ferrea (LF) is a medicinal plant that holds many pharmacological properties. We evaluated the antinociceptive effect in the LF aqueous seed extract and Lipidic Portion of Libidibia ferrea (LPLF), partially elucidating their mechanisms. Histochemical tests and Gas chromatography of the LPLF were performed to characterize its fatty acids. Acetic acid-induced abdominal constriction, formalin-induced pain, and hot-plate test in mice were employed in the study. In all experiments, aqueous extract or LPLF was administered systemically at the doses of 1, 5, and 10 mg/kg. LF aqueous seed extract and LPLF demonstrated a dose-dependent antinociceptive effect in all tests indicating both peripheral anti-inflammatory and central analgesia properties. Also, the use of atropine (5 mg/kg), naloxone (5 mg/kg) in the abdominal writhing test was able to reverse the antinociceptive effect of the LPLF, indicating that at least one of LF lipids components is responsible for the dose related antinociceptive action in chemical and thermal models of nociception in mice. Together, the present results suggested that Libidibia ferrea induced antinociceptive activity is possibly related to its ability to inhibit opioid, cholinergic receptors, and cyclooxygenase-2 pathway, since its main component, linoleic acid, has been demonstrated to produce such effect in previous studies