57 research outputs found

    Evaluation of antibacterial, antifungal and modulatory activity of methanol and ethanol extracts of Padina sanctae-crucis

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    Background: Multi-resistant microorganisms such as Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Candida tropicalis e Candida krusei are the main causes of microbial infections. Padina sanctae-crucis is a seaweed often used to check the contamination of ecosystems by materials such as heavy metals, but studies of the antimicrobial activity of the same seaweed were not found.Methods: The tests for the minimum inhibitory concentration and   modulation of microbial resistance, with the use of ethanolic and  methanolic extracts of Padina Sanctae-cruces combined with drugs of the class of aminoglycosides and antifungal were used to evaluate the activity against the cited microorganisms.Results: Was observed a modulation of antibiotic activity between the natural products and the E. coli and S. aureus strains, indicating a synergism and antagonism respectively.Conclusions: The results showed a moderate modulatory effect against some microorganisms studied.Keywords: multi-resistant microorganisms, modulation, Padina Sanctae-crucis, antimicrobial activity

    Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

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    Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation

    A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from Brazil

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    This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil

    Foundation Pattern, Productivity and Colony Success of the Paper Wasp, Polistes versicolor

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    Polistes versicolor (Olivier) (Hymenoptera: Vespidae) colonies are easily found in anthropic environments; however there is little information available on biological, ecological and behavioral interactions of this species under these environmental conditions. The objective of this work was to characterize the foundation pattern, the productivity, and the success of colonies of P. versicolor in anthropic environments. From August 2003 to December 2004, several colonies were studied in the municipal district of Juiz de Fora, Southeastern Brazil. It was possible to determine that before the beginning of nest construction the foundress accomplishes recognition flights in the selected area, and later begins the construction of the peduncle and the first cell. As soon as new cells are built, the hexagonal outlines appear and the peduncle is reinforced. Foundation of nests on gypsum plaster was significantly larger (p < 0.0001; χ2 test) in relation to the other types of substrate, revealing the synantropism of the species. On average, the P. versicolor nest presents 244.2 ± 89.5 (100–493) cells and a medium production of 171.67 ± 109.94 (37–660) adults. Cells that produced six individuals were verified. Usually, new colonies were founded by an association of females, responsible for the success of 51.5%. Although these results enlarge knowledge on the foundation pattern of P. versicolor in anthropic environments, other aspects of the foundation process require further investigation

    Ranking species in mutualistic networks

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    Understanding the architectural subtleties of ecological networks, believed to confer them enhanced stability and robustness, is a subject of outmost relevance. Mutualistic interactions have been profusely studied and their corresponding bipartite networks, such as plant-pollinator networks, have been reported to exhibit a characteristic “nested” structure. Assessing the importance of any given species in mutualistic networks is a key task when evaluating extinction risks and possible cascade effects. Inspired in a recently introduced algorithm –similar in spirit to Google's PageRank but with a built-in non-linearity– here we propose a method which –by exploiting their nested architecture– allows us to derive a sound ranking of species importance in mutualistic networks. This method clearly outperforms other existing ranking schemes and can become very useful for ecosystem management and biodiversity preservation, where decisions on what aspects of ecosystems to explicitly protect need to be made
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