Utilização de terapia fotodinâmica e óleos essenciais no controle In Vitro de Acinetobacter Baumannii e Pseudomonas Aeruginosa

O aumento da incidência de bactérias resistentes às múltiplas classes de antimicrobianos, principalmente Gram-negativas de prevalência nosocomial, têm se mostrado como grande ameaça à saúde pública internacional. O desenvolvimento de terapias alternativas que não induzem resistência bacteriana se mostra necessária e promissora. Este estudo objetivou avaliar a ação de extratos naturais derivados de plantas e terapia fotodinâmica no controle Pseudomonas aeruginosa e Acinetobacter baumannii cultivadas in vitro, através da determinação de concentrações inibitórias e bactericidas mínimas, e quantificação de unidades formadoras de colônias após exposição dos respectivos agentes, variando em tempo e concentração. Evidenciou-se ação dos extratos de limão taiti, tangerina e laranja amarga em diferentes concentrações. Neste estudo, a tangerina (Citrus tangerina) causou efeito inibitório e bactericida com menor quantidade em ambas as bactérias quando comparada aos outros extratos, porém, com maior concentração necessária na A. baumannii quando comparada à P. aeruginosa. Em relação à terapia fotodinâmica, foi evidenciada redução expressiva na quantidade de bactérias existentes no ambiente exposto, principalmente por maior quantidade de tempo, porem, sem garantir efeito inativação total de tais bactérias. A atividade antimicrobinada evidenciada no estudo, de ambas as terapias, demonstra a possibilidade do desenvolvimento de agentes adjuvantes no controle de replicação bacteriana com baixo custo e boa eficácia, sem risco de resistência bacteriana adicional

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

ATLANTIC-PRIMATES: a dataset of communities and occurrences of primates in the Atlantic Forests of South America

Primates play an important role in ecosystem functioning and offer critical insights into human evolution, biology, behavior, and emerging infectious diseases. There are 26 primate species in the Atlantic Forests of South America, 19 of them endemic. We compiled a dataset of 5,472 georeferenced locations of 26 native and 1 introduced primate species, as hybrids in the genera Callithrix and Alouatta. The dataset includes 700 primate communities, 8,121 single species occurrences and 714 estimates of primate population sizes, covering most natural forest types of the tropical and subtropical Atlantic Forest of Brazil, Paraguay and Argentina and some other biomes. On average, primate communities of the Atlantic Forest harbor 2 ± 1 species (range = 1–6). However, about 40% of primate communities contain only one species. Alouatta guariba (N = 2,188 records) and Sapajus nigritus (N = 1,127) were the species with the most records. Callicebus barbarabrownae (N = 35), Leontopithecus caissara (N = 38), and Sapajus libidinosus (N = 41) were the species with the least records. Recorded primate densities varied from 0.004 individuals/km 2 (Alouatta guariba at Fragmento do Bugre, Paraná, Brazil) to 400 individuals/km 2 (Alouatta caraya in Santiago, Rio Grande do Sul, Brazil). Our dataset reflects disparity between the numerous primate census conducted in the Atlantic Forest, in contrast to the scarcity of estimates of population sizes and densities. With these data, researchers can develop different macroecological and regional level studies, focusing on communities, populations, species co-occurrence and distribution patterns. Moreover, the data can also be used to assess the consequences of fragmentation, defaunation, and disease outbreaks on different ecological processes, such as trophic cascades, species invasion or extinction, and community dynamics. There are no copyright restrictions. Please cite this Data Paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. © 2018 by the The Authors. Ecology © 2018 The Ecological Society of Americ

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

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