Institution

# Federal University of Pernambuco

Education•Recife, Brazil•

About: Federal University of Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Species richness. The organization has 22973 authors who have published 35124 publications receiving 426579 citations.

Topics: Population, Species richness, Context (language use), Artificial neural network, Software development

##### Papers published on a yearly basis

##### Papers

More filters

••

Mohammad H. Forouzanfar

^{1}, Lily Alexander, H. Ross Anderson, Victoria F Bachman^{1}+733 more•Institutions (289)TL;DR: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) as discussed by the authors provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.

5,668 citations

••

TL;DR: In this article, the authors proposed a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters, which is useful for situations where the variable of interest is continuous and restricted to the interval (0, 1) and is related to other variables through a regression structure.

Abstract: This paper proposes a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters. The proposed model is useful for situations where the variable of interest is continuous and restricted to the interval (0, 1) and is related to other variables through a regression structure. The regression parameters of the beta regression model are interpretable in terms of the mean of the response and, when the logit link is used, of an odds ratio, unlike the parameters of a linear regression that employs a transformed response. Estimation is performed by maximum likelihood. We provide closed-form expressions for the score function, for Fisher's information matrix and its inverse. Hypothesis testing is performed using approximations obtained from the asymptotic normality of the maximum likelihood estimator. Some diagnostic measures are introduced. Finally, practical applications that employ real data are presented and discussed.

2,228 citations

••

Andrew G. Clark

^{1}, Michael B. Eisen^{2}, Michael B. Eisen^{3}, Douglas Smith +426 more•Institutions (70)TL;DR: These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution.

Abstract: Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

2,057 citations

••

TL;DR: It is shown that, when the target sites are sparse and can be visited any number of times, an inverse square power-law distribution of flight lengths, corresponding to Lévy flight motion, is an optimal strategy.

Abstract: We address the general question of what is the best statistical strategy to adapt in order to search efficiently for randomly located objects ('target sites'). It is often assumed in foraging theory that the flight lengths of a forager have a characteristic scale: from this assumption gaussian, Rayleigh and other classical distributions with well-defined variances have arisen. However, such theories cannot explain the long-tailed power-law distributions of flight lengths or flight times that are observed experimentally. Here we study how the search efficiency depends on the probability distribution of flight lengths taken by a forager that can detect target sites only in its limited vicinity. We show that, when the target sites are sparse and can be visited any number of times, an inverse square power-law distribution of flight lengths, corresponding to Levy flight motion, is an optimal strategy. We test the theory by analysing experimental foraging data on selected insect, mammal and bird species, and find that they are consistent with the predicted inverse square power-law distributions.

1,416 citations

••

TL;DR: In this paper, the development of efficient light conversion molecular devices (LCMDs) based on lanthanide complexes is reviewed, with emphasis on the work of our group, who have adopted a strategy based upon both theoretical and experimental (synthesis and methodological) investigations.

1,401 citations

##### Authors

Showing all 23185 results

Name | H-index | Papers | Citations |
---|---|---|---|

Glyn Lewis | 113 | 734 | 49316 |

Patrick Couvreur | 111 | 678 | 56735 |

José A. Teixeira | 101 | 1414 | 47329 |

João Rocha | 93 | 1521 | 49472 |

Miguel B. Araújo | 92 | 238 | 50049 |

Carlos Augusto Monteiro | 83 | 369 | 27114 |

Luís D. Carlos | 75 | 544 | 22063 |

Thomas Reps | 75 | 349 | 22625 |

Pedro Rodriguez | 67 | 496 | 24551 |

David Andreu | 63 | 512 | 15568 |

Michael G. Stabin | 61 | 279 | 13617 |

Marcelo Tabarelli | 61 | 181 | 12338 |

Ruxandra Gref | 60 | 171 | 22488 |

Ulysses Paulino Albuquerque | 59 | 397 | 12037 |

Cristina W. Nogueira | 59 | 503 | 16655 |