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Institution

Universidade Federal de Minas Gerais

EducationBelo Horizonte, Minas Gerais, Brazil
About: Universidade Federal de Minas Gerais is a education organization based out in Belo Horizonte, Minas Gerais, Brazil. It is known for research contribution in the topics: Population & Immune system. The organization has 41631 authors who have published 75688 publications receiving 1249905 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, a Mottinsulator phase of atom-photon excitations (polaritons) can arise in an array of individually addressable coupled electromagnetic cavities when each of these cavities is coupled resonantly to a single two-level system (atom, quantum dot, or Cooper pair).
Abstract: We propose a physical system where photons could exhibit strongly correlated effects. We demonstrate how a Mott-insulator phase of atom-photon excitations (polaritons) can arise in an array of individually addressable coupled electromagnetic cavities when each of these cavities is coupled resonantly to a single two-level system (atom, quantum dot, or Cooper pair). This Mott phase is characterized by the same integral number of net polaritonic excitations with photon blockade providing the required repulsion between the excitations in each site. Detuning the atomic and photonic frequencies suppresses this effect and induces a transition to a photonic superfluid. Finally, on resonance the system can straightforwardly simulate the dynamics of many-body spin systems.

505 citations

Journal ArticleDOI
04 Dec 2009-Science
TL;DR: According to this analysis, these recent developments finally make feasible the end of deforestation in the Brazilian Amazon, which could result in a 2 to 5% reduction in global carbon emissions.
Abstract: Brazil has two major opportunities to end the clearing of its Amazon forest and to reduce global greenhouse gas emissions substantially. The first is its formal announcement within United Nations climate treaty negotiations in 2008 of an Amazon deforestation reduction target, which prompted Norway to commit $1 billion if it sustains progress toward this target ( 1 ). The second is a widespread marketplace transition within the beef and soy industries, the main drivers of deforestation, to exclude Amazon deforesters from their supply chains ( 2 ) [supplementary online material (SOM), section (§) 4]. According to our analysis, these recent developments finally make feasible the end of deforestation in the Brazilian Amazon, which could result in a 2 to 5% reduction in global carbon emissions. The $7 to $18 billion beyond Brazil's current budget outlays that may be needed to stop the clearing [a range intermediate to previous cost estimates ( 3 , 4 )] could be provided by the REDD (Reducing Emissions from Deforestation and Forest Degradation) mechanism for compensating deforestation reduction that is under negotiation within the UN climate treaty ( 5 ), or by payments for tropical forest carbon credits under a U.S. cap-and-trade system ( 6 ).

501 citations

Proceedings ArticleDOI
14 May 2017
TL;DR: This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators, and the results are promising.
Abstract: Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. For that goal, a prediction model was built, and a series of experiments were executed and theirs results analyzed against a number of metrics to assess if this type of algorithm presents and improvements when compared to other Machine Learning methods and investment strategies. The results that were obtained are promising, getting up to an average of 55.9% of accuracy when predicting if the price of a particular stock is going to go up or not in the near future.

495 citations

Journal ArticleDOI
TL;DR: The data suggest that subjects with periodontitis, diabetes, and poor oral hygiene were more prone to develop peri-implantitis.
Abstract: Objectives: The aim of this study was to verify the prevalence of peri-implant disease and analyse possible risk variables associated with peri-implant mucositis and peri-implantitis. The study group consisted of 212 partially edentulous subjects rehabilitated with osseointegrated implants. Material and Methods: The implants placed were examined clinically and radiographically to assess the peri-implant status. The degree of association between peri-implant disease and various independent variables was investigated using a multinomial regression analysis. Results: The prevalence of peri-implant mucositis and peri-implantitis were 64.6% and 8.9%, respectively. In univariate modelling, healthy peri-implant subjects presented lower plaque scores, less periodontal bleeding on probing, and less time elapsed since placement of supra-structures. In multivariate analyses, the risk variables associated with increased odds for having peri-implant disease included: gender, plaque scores, and periodontal bleeding on probing. Presence of periodontitis and diabetes were statistically associated with increased risk of peri-implantitis. The only two factors, which did not contribute to the presence of the disease, were the time elapsed since placement of supra-structures and the frequency of visits for maintenance care. Conclusion: Our data suggest that subjects with periodontitis, diabetes, and poor oral hygiene were more prone to develop peri-implantitis.

494 citations

Journal ArticleDOI
TL;DR: It is demonstrated that Ang-(1-7), through Mas, stimulates eNOS activation and NO production via Akt-dependent pathways, and the importance of the Ang-( 1-7)/Mas axis as a putative regulator of endothelial function is highlighted.
Abstract: Angiotensin-(1-7) [Ang-(1-7)] causes endothelial-dependent vasodilation mediated, in part, by NO release. However, the molecular mechanisms involved in endothelial NO synthase (eNOS) activation by Ang-(1-7) remain unknown. Using Chinese hamster ovary cells stably transfected with Mas cDNA (Chinese hamster ovary-Mas), we evaluated the underlying mechanisms related to receptor Mas-mediated posttranslational eNOS activation and NO release. We further examined the Ang-(1-7) profile of eNOS activation in human aortic endothelial cells, which constitutively express the Mas receptor. Chinese hamster ovary-Mas cells and human aortic endothelial cell were stimulated with Ang-(1-7; 10(-7) mol/L; 1 to 30 minutes) in the absence or presence of A-779 (10(-6) mol/L). Additional experiments were performed in the presence of the phosphatidylinositol 3-kinase inhibitor wortmannin (10(-6) mol/L). Changes in eNOS (at Ser1177/Thr495 residues) and Akt phosphorylation were evaluated by Western blotting. NO release was measured using both the fluorochrome 2,3-diaminonaphthalene and an NO analyzer. Ang-(1-7) significantly stimulated eNOS activation (reciprocal phosphorylation/dephosphorylation at Ser1177/Thr495) and induced a sustained Akt phosphorylation (P<0.05). Concomitantly, a significant increase in NO release was observed (2-fold increase in relation to control). These effects were blocked by A-779. Wortmannin suppressed eNOS activation in both Chinese hamster ovary-Mas and human aortic endothelial cells. Our findings demonstrate that Ang-(1-7), through Mas, stimulates eNOS activation and NO production via Akt-dependent pathways. These novel data highlight the importance of the Ang-(1-7)/Mas axis as a putative regulator of endothelial function.

489 citations


Authors

Showing all 42077 results

NameH-indexPapersCitations
Michael Marmot1931147170338
Pulickel M. Ajayan1761223136241
Alan D. Lopez172863259291
Jens Nielsen1491752104005
Mildred S. Dresselhaus136762112525
Jing Kong12655372354
Mauricio Terrones11876061202
Michael Brammer11842446763
Terence G. Langdon117115861603
Caroline A. Sabin10869044233
Michael Brauer10648073664
Michael Bader10373537525
Michael S. Strano9848060141
Pablo Jarillo-Herrero9124539171
Riichiro Saito9150248869
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023111
2022624
20215,708
20205,955
20195,269
20185,020