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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: The growth of WikiPathways over the last three years is shown, the new communities and collaborations of pathway authors and curators are highlighted, and various technologies to connect to external resources and initiatives are described.
Abstract: WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

378 citations

Journal ArticleDOI
TL;DR: The results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications.
Abstract: A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts.

377 citations

Journal ArticleDOI
TL;DR: It is now possible to precisely identify few-layered STMD and new high frequency first order A′1 and A1g modes appear, explaining recently reported experimental data for WSe2, MoSe2 and MoS2.
Abstract: Although the main Raman features of semiconducting transition metal dichalcogenides are well known for the monolayer and bulk, there are important differences exhibited by few layered systems which have not been fully addressed. WSe2 samples were synthesized and ab-initio calculations carried out. We calculated phonon dispersions and Raman-active modes in layered systems: WSe2, MoSe2, WS2 and MoS2 ranging from monolayers to five-layers and the bulk. First, we confirmed that as the number of layers increase, the E', E″ and E2g modes shift to lower frequencies, and the A'1 and A1g modes shift to higher frequencies. Second, new high frequency first order A'1 and A1g modes appear, explaining recently reported experimental data for WSe2, MoSe2 and MoS2. Third, splitting of modes around A'1 and A1g is found which explains those observed in MoSe2. Finally, exterior and interior layers possess different vibrational frequencies. Therefore, it is now possible to precisely identify few-layered STMD.

373 citations

Proceedings ArticleDOI
17 May 2004
TL;DR: A domain-oriented approach to Web data extraction is presented and its application to automatically extracting news from Web sites is discussed, based on a highly efficient tree structure analysis that produces very effective results.
Abstract: The Web poses itself as the largest data repository ever available in the history of humankind. Major efforts have been made in order to provide efficient access to relevant information within this huge repository of data. Although several techniques have been developed to the problem of Web data extraction, their use is still not spread, mostly because of the need for high human intervention and the low quality of the extraction results.In this paper, we present a domain-oriented approach to Web data extraction and discuss its application to automatically extracting news from Web sites. Our approach is based on a highly efficient tree structure analysis that produces very effective results. We have tested our approach with several important Brazilian on-line news sites and achieved very precise results, correctly extracting 87.71% of the news in a set of 4088 pages distributed among 35 different sites.

370 citations

Journal ArticleDOI
TL;DR: Older people's adherence to exercise programs is most commonly measured with dropout and attendance rates and is associated with a range of program and personal factors.

365 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