scispace - formally typeset
Search or ask a question
Institution

University of Lisbon

EducationLisbon, Lisboa, Portugal
About: University of Lisbon is a education organization based out in Lisbon, Lisboa, Portugal. It is known for research contribution in the topics: Population & Context (language use). The organization has 19122 authors who have published 48503 publications receiving 1102623 citations. The organization is also known as: Universidade de Lisboa & Lisbon University.


Papers
More filters
Journal ArticleDOI
TL;DR: A short review of the State-of-the-Art of the connectivity concept is provided, from which it is concluded that scientists have been struggling to find a way to quantify connectivity so far.

180 citations

Journal ArticleDOI
TL;DR: In this article, a new consistent way of parametrizing simultaneously local and non-local turbulent transport for the convective atmospheric boundary layer has been proposed and tested for the clear boundary layer.
Abstract: Recently, a new consistent way of parametrizing simultaneously local and non-local turbulent transport for the convective atmospheric boundary layer has been proposed and tested for the clear boundary layer. This approach assumes that in the convective boundary layer the subgrid-scale fluxes result from two different mixing scales: small eddies, that are parametrized by an eddy-diffusivity approach, and thermals, which are represented by a mass-flux contribution. Since the interaction between the cloud layer and the underlying sub-cloud layer predominantly takes place through strong updraughts, this approach offers an interesting avenue of establishing a unified description of the turbulent transport in the cumulus-topped boundary layer. This paper explores the possibility of such a new approach for the cumulus-topped boundary layer. In the sub-cloud and cloud layers, the mass-flux term represents the effect of strong updraughts. These are modelled by a simple entraining parcel, which determines the mean properties of the strong updraughts, the boundary-layer height, the lifting condensation level and cloud top. The residual smaller-scale turbulent transport is parametrized with an eddy-diffusivity approach that uses a turbulent kinetic energy closure. The new scheme is implemented and tested in the research model MesoNH.

180 citations

Journal ArticleDOI
03 Aug 2011-PLOS ONE
TL;DR: It is suggested that miR-34a is required for proper neuronal differentiation, in part, by targeting SIRT1 and modulating p53 activity, thus reinforcing the role of p53 during neural differentiation.
Abstract: Background MicroRNAs (miRNAs or miRs) participate in the regulation of several biological processes, including cell differentiation. Recently, miR-34a has been implicated in the differentiation of monocyte-derived dendritic cells, human erythroleukemia cells, and mouse embryonic stem cells. In addition, members of the miR-34 family have been identified as direct p53 targets. However, the function of miR-34a in the control of the differentiation program of specific neural cell types remains largely unknown. Here, we investigated the role of miR-34a in regulating mouse neural stem (NS) cell differentiation.

180 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Dale Charles Abbott3  +3001 moreInstitutions (220)
TL;DR: In this paper, the decays of B0 s! + and B0! + have been studied using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016.
Abstract: A study of the decays B0 s ! + and B0 ! + has been performed using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016. Since the detector resolut ...

180 citations

Journal ArticleDOI
TL;DR: Characteristics of successful and failed non-native fishes in a Mediterranean-climate area, the Iberian Peninsula, for three stages of the invasion process: establishment, spread and integration are determined, demonstrating that successful invaders have a combination of biological traits that may favor success at all invasion stages.
Abstract: Freshwater ecosystems are seriously imperiled by the spread of non-native fishes thus establishing profiles of their life-history characteristics is an emerging tool for developing conservation and management strategies. We did a first approach to determine characteristics of successful and failed non-native fishes in a Mediterranean-climate area, the Iberian Peninsula, for three stages of the invasion process: establishment, spread and integration. Using general linear models, we established which characteristics are most important for success at each invasion stage. Prior invasion success was a good predictor for all the stages of the invasion process. Biological variables relevant for more than one invasion stage were maximum adult size and size of native range. Despite these common variables, all models produced a different set of variables important for a successful invasion, demonstrating that successful invaders have a combination of biological traits that may favor success at all invasion stages. However, some differences were found in relation to published studies on fish invasions in other Mediterranean-climate areas, suggesting that characteristics of the recipient ecosystem are as relevant as the characteristics of the invading species.

180 citations


Authors

Showing all 19716 results

NameH-indexPapersCitations
Joao Seixas1531538115070
A. Gomes1501862113951
Marco Costa1461458105096
António Amorim136147796519
Osamu Jinnouchi13588586104
P. Verdier133111183862
Andy Haas132109687742
Wendy Taylor131125289457
Steve McMahon13087878763
Timothy Andeen129106977593
Heather Gray12996680970
Filipe Veloso12888775496
Nuno Filipe Castro12896076945
Oliver Stelzer-Chilton128114179154
Isabel Marian Trigger12897477594
Network Information
Related Institutions (5)
VU University Amsterdam
75.6K papers, 3.4M citations

91% related

University of Padua
114.8K papers, 3.6M citations

91% related

University of Bologna
115.1K papers, 3.4M citations

91% related

University of Groningen
69.1K papers, 2.9M citations

91% related

Utrecht University
139.3K papers, 6.2M citations

91% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023247
2022828
20214,521
20204,517
20193,810
20183,617