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Institution

Sapienza University of Rome

EducationRome, Lazio, Italy
About: Sapienza University of Rome is a education organization based out in Rome, Lazio, Italy. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 62002 authors who have published 155468 publications receiving 4397244 citations. The organization is also known as: La Sapienza & Università La Sapienza di Roma.


Papers
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Proceedings ArticleDOI
09 Oct 2006
TL;DR: An efficient collision detection method that uses only proprioceptive robot sensors and provides also directional information for a safe robot reaction after collision is presented.
Abstract: A robot manipulator sharing its workspace with humans should be able to quickly detect collisions and safely react for limiting injuries due to physical contacts. In the absence of external sensing, relative motions between robot and human are not predictable and unexpected collisions may occur at any location along the robot arm. Based on physical quantities such as total energy and generalized momentum of the robot manipulator, we present an efficient collision detection method that uses only proprioceptive robot sensors and provides also directional information for a safe robot reaction after collision. The approach is first developed for rigid robot arms and then extended to the case of robots with elastic joints, proposing different reaction strategies. Experimental results on collisions with the DLR-III lightweight manipulator are reported.

650 citations

Journal ArticleDOI
Dominik Sturm1, Dominik Sturm2, Brent A. Orr3, Umut H. Toprak2, Volker Hovestadt2, David T.W. Jones2, David Capper1, David Capper2, Martin Sill2, Ivo Buchhalter2, Paul A. Northcott2, Irina Leis1, Marina Ryzhova, Christian Koelsche2, Christian Koelsche1, Elke Pfaff1, Elke Pfaff2, Sariah Allen3, Gnanaprakash Balasubramanian2, Barbara C. Worst1, Barbara C. Worst2, Kristian W. Pajtler2, Sebastian Brabetz2, Pascal Johann1, Pascal Johann2, Felix Sahm1, Felix Sahm2, Jüri Reimand4, Jüri Reimand5, Alan Mackay6, Diana Carvalho6, Marc Remke5, Joanna J. Phillips7, Arie Perry7, Cynthia Cowdrey7, Rachid Drissi8, Maryam Fouladi8, Felice Giangaspero9, Maria Łastowska10, Wiesława Grajkowska10, Wolfram Scheurlen11, Torsten Pietsch12, Christian Hagel13, Johannes Gojo14, Daniela Lötsch14, Walter Berger14, Irene Slavc14, Christine Haberler14, Anne Jouvet15, Stefan Holm16, Silvia Hofer, Marco Prinz17, Catherine Keohane18, Iris Fried19, Christian Mawrin20, David Scheie21, Bret C. Mobley22, Matthew Schniederjan, Mariarita Santi23, Anna Maria Buccoliero11, Sonika Dahiya24, Christof M. Kramm25, André O. von Bueren25, Katja von Hoff13, Stefan Rutkowski13, Christel Herold-Mende1, Michael C. Frühwald26, Till Milde1, Till Milde2, Martin Hasselblatt27, Pieter Wesseling28, Pieter Wesseling29, Jochen Rößler30, Ulrich Schüller31, Martin Ebinger, Jens Schittenhelm32, Stephan Frank33, Rainer Grobholz, Istvan Vajtai, Volkmar Hans, Reinhard Schneppenheim13, Karel Zitterbart34, V. Peter Collins35, Eleonora Aronica36, Pascale Varlet, Stéphanie Puget37, Christelle Dufour38, Jacques Grill38, Dominique Figarella-Branger39, Marietta Wolter40, Martin U. Schuhmann32, Tarek Shalaby11, Michael A. Grotzer11, Timothy E. Van Meter41, Camelia M. Monoranu42, Jörg Felsberg40, Guido Reifenberger40, Matija Snuderl43, Lynn Ann Forrester43, Jan Koster36, Rogier Versteeg36, Richard Volckmann36, Peter van Sluis36, Stephan Wolf2, Tom Mikkelsen44, Amar Gajjar3, Kenneth Aldape45, Andrew S. Moore46, Michael D. Taylor5, Chris Jones6, Nada Jabado47, Matthias A. Karajannis43, Roland Eils, Matthias Schlesner2, Peter Lichter2, Andreas von Deimling1, Andreas von Deimling2, Stefan M. Pfister2, Stefan M. Pfister1, David W. Ellison3, Andrey Korshunov2, Andrey Korshunov1, Marcel Kool2 
25 Feb 2016-Cell
TL;DR: It is demonstrated that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors.

648 citations

Journal ArticleDOI
TL;DR: Evidence is provided that GLP-1 added to freshly isolated human islets preserves morphology and function and inhibits cell apoptosis, and better-preserved three-dimensional islet morphology in the GLp-1-treated islets, compared with controls.
Abstract: The peptide hormone, glucagon-like peptide 1 (GLP-1), has been shown to increase glucose-dependent insulin secretion, enhance insulin gene transcription, expand islet cell mass, and inhibit -cell apoptosis in animal models of diabetes. The aim of the present study was to evaluate whether GLP-1 could improve function and inhibit apoptosis in freshly isolated human islets. Human islets were cultured fo r5di n thepresence, or absence, of GLP-1 (10 nM, added every 12 h) and studied for viability and expression of proapoptotic (caspase-3) and antiapoptotic factors (bcl-2) as well as glucose-dependent insulin production. We observed better-preserved three-dimensional islet morphology in the GLP-1-treated islets, compared with controls. Nuclear condensation, a feature of cell apoptosis, was inhibited by GLP-1. The reduction in the number of apoptotic cells in GLP-1-treated islets was particularly evident at d 3 (6.1% apoptotic nuclei in treated cultures vs. 15.5% in controls; P < 0.01) and at d 5 (8.9 vs. 18.9%; P < 0.01). The antiapoptotic effect of GLP-1 was associated with the downregulation of active caspase-3 (P < 0.001) and the up-regulation of bcl-2 (P < 0.01). The effect of GLP-1 on the intracellular levels of bcl-2 and caspase-3 was observed at the mRNA and protein levels. Intracellular insulin content was markedly enhanced in islets cultured with GLP-1 vs. control (P < 0.001, at d 5), and there was a parallel GLP-1-dependent potentiation of glucose-dependent insulin secretion (P < 0.01 at d 3; P < 0.05 at d 5). Our findings provide evidence that GLP-1 added to freshly isolated human islets preserves morphology and function and inhibits cell apoptosis. (Endocrinology 144: 5149 –5158, 2003)

645 citations

Journal ArticleDOI
TL;DR: In this article, a detailed description of the analysis used by the CMS Collaboration in the search for the standard model Higgs boson in pp collisions at the LHC, which led to the observation of a new boson.
Abstract: A detailed description is reported of the analysis used by the CMS Collaboration in the search for the standard model Higgs boson in pp collisions at the LHC, which led to the observation of a new boson. The data sample corresponds to integrated luminosities up to 5.1 inverse femtobarns at sqrt(s) = 7 TeV, and up to 5.3 inverse femtobarns at sqrt(s) = 8 TeV. The results for five Higgs boson decay modes gamma gamma, ZZ, WW, tau tau, and bb, which show a combined local significance of 5 standard deviations near 125 GeV, are reviewed. A fit to the invariant mass of the two high resolution channels, gamma gamma and ZZ to 4 ell, gives a mass estimate of 125.3 +/- 0.4 (stat) +/- 0.5 (syst) GeV. The measurements are interpreted in the context of the standard model Lagrangian for the scalar Higgs field interacting with fermions and vector bosons. The measured values of the corresponding couplings are compared to the standard model predictions. The hypothesis of custodial symmetry is tested through the measurement of the ratio of the couplings to the W and Z bosons. All the results are consistent, within their uncertainties, with the expectations for a standard model Higgs boson.

643 citations


Authors

Showing all 62745 results

NameH-indexPapersCitations
Charles A. Dinarello1901058139668
Gregory Y.H. Lip1693159171742
Peter A. R. Ade1621387138051
H. Eugene Stanley1541190122321
Suvadeep Bose154960129071
P. de Bernardis152680117804
Bart Staels15282486638
Alessandro Melchiorri151674116384
Andrew H. Jaffe149518110033
F. Piacentini149531108493
Subir Sarkar1491542144614
Albert Bandura148255276143
Carlo Rovelli1461502103550
Robert C. Gallo14582568212
R. Kowalewski1431815135517
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023405
20221,106
20219,796
20209,753
20198,332
20187,615